open/deepresearch
1
0
Fork 0
mirror of https://github.com/furyhawk/deepresearch.git synced 2026-07-20 09:47:59 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add initial meta and workspace documentation for agentic AI research

Browse Source 
- Created meta.json to track session details and research todos.
- Added DEEP.md to outline the research workflow and file organization.
- Introduced MEMORY.md for persistent notes and context management.
This commit is contained in:
furyhawk
2026-06-19 12:18:32 +08:00
parent 50e4d4fc3d
commit 85c286b72f
17 changed files with 8244 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/deepresearch/agent.py
+1
View File
@@ -425,6 +425,7 @@ def create_research_agent(
hooks=HOOKS,
middleware=[ForgiveWriteTodosCapability(), *(middleware or [])]
+ ([RateLimitRetryCapability()] if USE_RATE_LIMITER else []),
include_memory=False,
context_manager=True,
context_manager_max_tokens=200_000,
patch_tool_calls=True,
workspaces/49b65705-4300-4795-8b9a-306ce20018b1/events.jsonl
+485
View File
@@ -0,0 +1,485 @@
{"type": "session_created", "session_id": "49b65705-4300-4795-8b9a-306ce20018b1", "_ts": "2026-06-19T02:34:58.197885+00:00"}
{"type": "canvas_ready", "session_id": "49b65705-4300-4795-8b9a-306ce20018b1", "_ts": "2026-06-19T02:34:58.212039+00:00"}
{"type": "user_message", "content": "research on architect agentic ai solution.", "_ts": "2026-06-19T02:34:58.212139+00:00"}
{"type": "start", "_ts": "2026-06-19T02:34:58.212418+00:00"}
{"type": "status", "content": "Processing...", "_ts": "2026-06-19T02:34:58.220602+00:00"}
{"type": "status", "content": "Researching...", "_ts": "2026-06-19T02:34:58.230287+00:00"}
{"type": "tool_call_start", "tool_name": "task", "tool_call_id": "UDgBI27fuQnEq2ChwEk2kpyGskhPBOQF", "_ts": "2026-06-19T02:35:45.429244+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"description", "_ts": "2026-06-19T02:35:45.491821+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\":", "_ts": "2026-06-19T02:35:45.556351+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:35:45.621540+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "Create", "_ts": "2026-06-19T02:35:45.686234+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " a", "_ts": "2026-06-19T02:35:45.753350+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " research", "_ts": "2026-06-19T02:35:45.816467+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " plan", "_ts": "2026-06-19T02:35:45.878117+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " for", "_ts": "2026-06-19T02:35:45.940497+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " '", "_ts": "2026-06-19T02:35:46.002598+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "Architect", "_ts": "2026-06-19T02:35:46.065308+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "ing", "_ts": "2026-06-19T02:35:46.128409+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Agent", "_ts": "2026-06-19T02:35:46.190431+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "ic", "_ts": "2026-06-19T02:35:46.252091+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " AI", "_ts": "2026-06-19T02:35:46.313506+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Solutions", "_ts": "2026-06-19T02:35:46.375228+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "'.", "_ts": "2026-06-19T02:35:46.437770+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Break", "_ts": "2026-06-19T02:35:46.500949+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " this", "_ts": "2026-06-19T02:35:46.563783+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " into", "_ts": "2026-06-19T02:35:46.626492+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " ", "_ts": "2026-06-19T02:35:46.686913+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "4", "_ts": "2026-06-19T02:35:46.747189+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:35:46.808075+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "5", "_ts": "2026-06-19T02:35:46.868842+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " focused", "_ts": "2026-06-19T02:35:46.929142+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " research", "_ts": "2026-06-19T02:35:46.990103+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " sub", "_ts": "2026-06-19T02:35:47.050373+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:35:47.110734+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "topics", "_ts": "2026-06-19T02:35:47.171989+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " (", "_ts": "2026-06-19T02:35:47.232827+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "e", "_ts": "2026-06-19T02:35:47.293161+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ".", "_ts": "2026-06-19T02:35:47.355157+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "g", "_ts": "2026-06-19T02:35:47.416115+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ".,", "_ts": "2026-06-19T02:35:47.476748+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " multi", "_ts": "2026-06-19T02:35:47.536627+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:35:47.597420+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "agent", "_ts": "2026-06-19T02:35:47.658168+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " architectures", "_ts": "2026-06-19T02:35:47.719201+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:35:47.782180+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " reasoning", "_ts": "2026-06-19T02:35:47.842425+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " patterns", "_ts": "2026-06-19T02:35:47.905164+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " like", "_ts": "2026-06-19T02:35:47.967780+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Re", "_ts": "2026-06-19T02:35:48.029182+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "Act", "_ts": "2026-06-19T02:35:48.090856+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "/", "_ts": "2026-06-19T02:35:48.152292+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "Reflection", "_ts": "2026-06-19T02:35:48.213393+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:35:48.273913+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " tool", "_ts": "2026-06-19T02:35:48.334255+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " use", "_ts": "2026-06-19T02:35:48.395099+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " &", "_ts": "2026-06-19T02:35:48.456186+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " memory", "_ts": "2026-06-19T02:35:48.519156+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:35:48.578784+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " orchestration", "_ts": "2026-06-19T02:35:48.639655+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " frameworks", "_ts": "2026-06-19T02:35:48.702297+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:35:48.763529+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " and", "_ts": "2026-06-19T02:35:48.823383+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " production", "_ts": "2026-06-19T02:35:48.884144+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " deployment", "_ts": "2026-06-19T02:35:48.946162+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "/", "_ts": "2026-06-19T02:35:49.007196+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "eval", "_ts": "2026-06-19T02:35:49.071426+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ").", "_ts": "2026-06-19T02:35:49.132365+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Ask", "_ts": "2026-06-19T02:35:49.192631+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " the", "_ts": "2026-06-19T02:35:49.252461+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " user", "_ts": "2026-06-19T02:35:49.312402+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " if", "_ts": "2026-06-19T02:35:49.372866+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " they", "_ts": "2026-06-19T02:35:49.434159+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " want", "_ts": "2026-06-19T02:35:49.494455+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " a", "_ts": "2026-06-19T02:35:49.554694+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " technical", "_ts": "2026-06-19T02:35:49.614940+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " deep", "_ts": "2026-06-19T02:35:49.674336+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:35:49.734033+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "dive", "_ts": "2026-06-19T02:35:49.793559+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " or", "_ts": "2026-06-19T02:35:49.854339+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " a", "_ts": "2026-06-19T02:35:49.915001+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " high", "_ts": "2026-06-19T02:35:49.975061+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:35:50.035419+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "level", "_ts": "2026-06-19T02:35:50.095746+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " overview", "_ts": "2026-06-19T02:35:50.156179+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ".", "_ts": "2026-06-19T02:35:50.216511+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:35:50.276823+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:35:50.337414+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"sub", "_ts": "2026-06-19T02:35:50.397573+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "agent", "_ts": "2026-06-19T02:35:50.457408+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "_", "_ts": "2026-06-19T02:35:50.517827+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "type", "_ts": "2026-06-19T02:35:50.578162+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\":", "_ts": "2026-06-19T02:35:50.637391+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:35:50.696988+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "planner", "_ts": "2026-06-19T02:35:50.757659+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:35:50.818018+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "}", "_ts": "2026-06-19T02:35:50.877459+00:00"}
{"type": "tool_start", "tool_name": "task", "args": "{\"description\":\"Create a research plan for 'Architecting Agentic AI Solutions'. Break this into 4-5 focused research sub-topics (e.g., multi-agent architectures, reasoning patterns like ReAct/Reflection, tool use & memory, orchestration frameworks, and production deployment/eval). Ask the user if they want a technical deep-dive or a high-level overview.\",\"subagent_type\":\"planner\"}", "_ts": "2026-06-19T02:35:51.006750+00:00"}
{"type": "status", "content": "Running planner subagent...", "_ts": "2026-06-19T02:35:51.006883+00:00"}
{"type": "tool_output", "tool_name": "task", "output": "Error executing task: AgentMemoryToolset 'deep-memory' defines a tool whose name conflicts with existing tool from AgentMemoryToolset 'deep-memory': 'read_memory'. Rename the tool or wrap the toolset in a `PrefixedToolset` to avoid name conflicts.", "_ts": "2026-06-19T02:35:51.011393+00:00"}
{"type": "middleware_event", "event": "tool_audit", "tool_name": "task", "total_calls": 1, "tools_breakdown": {"task": 1}, "_ts": "2026-06-19T02:35:51.011508+00:00"}
{"type": "status", "content": "Researching...", "_ts": "2026-06-19T02:35:51.011774+00:00"}
{"type": "tool_call_start", "tool_name": "write_todos", "tool_call_id": "Ohp1h4Li6lmYJKx1Hk9yZKhUudCyroFI", "_ts": "2026-06-19T02:35:52.874514+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"todos", "_ts": "2026-06-19T02:35:52.936486+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":[", "_ts": "2026-06-19T02:35:52.997889+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "{", "_ts": "2026-06-19T02:35:53.059022+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"active", "_ts": "2026-06-19T02:35:53.121074+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "_", "_ts": "2026-06-19T02:35:53.183461+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "form", "_ts": "2026-06-19T02:35:53.244523+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:35:53.306028+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:53.368565+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Planning", "_ts": "2026-06-19T02:35:53.428550+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " research", "_ts": "2026-06-19T02:35:53.489719+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " on", "_ts": "2026-06-19T02:35:53.552578+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Architect", "_ts": "2026-06-19T02:35:53.614213+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "ing", "_ts": "2026-06-19T02:35:53.675097+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Agent", "_ts": "2026-06-19T02:35:53.736198+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "ic", "_ts": "2026-06-19T02:35:53.797099+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " AI", "_ts": "2026-06-19T02:35:53.857236+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Solutions", "_ts": "2026-06-19T02:35:53.919956+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:53.981148+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:35:54.041184+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"content", "_ts": "2026-06-19T02:35:54.101964+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:35:54.163340+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:54.224265+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Create", "_ts": "2026-06-19T02:35:54.284880+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " research", "_ts": "2026-06-19T02:35:54.346837+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " plan", "_ts": "2026-06-19T02:35:54.407988+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " for", "_ts": "2026-06-19T02:35:54.468704+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Architect", "_ts": "2026-06-19T02:35:54.529642+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "ing", "_ts": "2026-06-19T02:35:54.589978+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Agent", "_ts": "2026-06-19T02:35:54.650879+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "ic", "_ts": "2026-06-19T02:35:54.712063+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " AI", "_ts": "2026-06-19T02:35:54.774216+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Solutions", "_ts": "2026-06-19T02:35:54.837667+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " using", "_ts": "2026-06-19T02:35:54.898079+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " a", "_ts": "2026-06-19T02:35:54.959609+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " planner", "_ts": "2026-06-19T02:35:55.020407+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " sub", "_ts": "2026-06-19T02:35:55.082808+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "agent", "_ts": "2026-06-19T02:35:55.145231+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:55.210131+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:35:55.271481+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"status", "_ts": "2026-06-19T02:35:55.333711+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:35:55.394994+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:55.456333+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "pending", "_ts": "2026-06-19T02:35:55.517735+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:55.579244+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "},{", "_ts": "2026-06-19T02:35:55.639293+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"active", "_ts": "2026-06-19T02:35:55.701110+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "_", "_ts": "2026-06-19T02:35:55.761565+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "form", "_ts": "2026-06-19T02:35:55.821518+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:35:55.882388+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:55.943843+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Research", "_ts": "2026-06-19T02:35:56.003981+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "ing", "_ts": "2026-06-19T02:35:56.064533+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " multi", "_ts": "2026-06-19T02:35:56.125246+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:35:56.186185+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "agent", "_ts": "2026-06-19T02:35:56.247307+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " architectures", "_ts": "2026-06-19T02:35:56.308613+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:56.370157+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:35:56.430515+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"content", "_ts": "2026-06-19T02:35:56.492124+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:35:56.553103+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:56.613837+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Research", "_ts": "2026-06-19T02:35:56.674112+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " multi", "_ts": "2026-06-19T02:35:56.735272+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:35:56.797595+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "agent", "_ts": "2026-06-19T02:35:56.858144+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " architectural", "_ts": "2026-06-19T02:35:56.920609+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " patterns", "_ts": "2026-06-19T02:35:56.981009+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " (", "_ts": "2026-06-19T02:35:57.041154+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "e", "_ts": "2026-06-19T02:35:57.102385+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ".", "_ts": "2026-06-19T02:35:57.163099+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "g", "_ts": "2026-06-19T02:35:57.223909+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ".,", "_ts": "2026-06-19T02:35:57.284134+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " hierarchical", "_ts": "2026-06-19T02:35:57.345325+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:35:57.405979+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " joint", "_ts": "2026-06-19T02:35:57.467152+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:35:57.527919+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " feder", "_ts": "2026-06-19T02:35:57.587429+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "ated", "_ts": "2026-06-19T02:35:57.648736+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ")", "_ts": "2026-06-19T02:35:57.709681+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:57.770658+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:35:57.831149+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"status", "_ts": "2026-06-19T02:35:57.891708+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:35:57.952214+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:58.012829+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "pending", "_ts": "2026-06-19T02:35:58.073981+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:58.134128+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "},{", "_ts": "2026-06-19T02:35:58.195262+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"active", "_ts": "2026-06-19T02:35:58.255664+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "_", "_ts": "2026-06-19T02:35:58.316783+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "form", "_ts": "2026-06-19T02:35:58.376684+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:35:58.436650+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:58.496843+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Research", "_ts": "2026-06-19T02:35:58.559027+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "ing", "_ts": "2026-06-19T02:35:58.619359+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " reasoning", "_ts": "2026-06-19T02:35:58.680313+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " and", "_ts": "2026-06-19T02:35:58.741989+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " planning", "_ts": "2026-06-19T02:35:58.802341+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " patterns", "_ts": "2026-06-19T02:35:58.863732+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:58.924557+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:35:58.986035+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"content", "_ts": "2026-06-19T02:35:59.047991+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:35:59.109252+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:35:59.170234+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Research", "_ts": "2026-06-19T02:35:59.231150+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " reasoning", "_ts": "2026-06-19T02:35:59.292476+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " patterns", "_ts": "2026-06-19T02:35:59.353268+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " like", "_ts": "2026-06-19T02:35:59.413727+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Re", "_ts": "2026-06-19T02:35:59.473755+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Act", "_ts": "2026-06-19T02:35:59.535196+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:35:59.595465+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Chain", "_ts": "2026-06-19T02:35:59.657992+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:35:59.716414+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "of", "_ts": "2026-06-19T02:35:59.777671+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:35:59.838214+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Thought", "_ts": "2026-06-19T02:35:59.904942+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:35:59.967218+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Reflection", "_ts": "2026-06-19T02:36:00.027175+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:00.086646+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " and", "_ts": "2026-06-19T02:36:00.149039+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Tree", "_ts": "2026-06-19T02:36:00.209544+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:36:00.269903+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "of", "_ts": "2026-06-19T02:36:00.334894+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:36:00.395638+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Thoughts", "_ts": "2026-06-19T02:36:00.456556+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:00.517082+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:00.578006+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"status", "_ts": "2026-06-19T02:36:00.638466+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:00.700272+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:00.761538+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "pending", "_ts": "2026-06-19T02:36:00.821850+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:00.882646+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "},{", "_ts": "2026-06-19T02:36:00.945328+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"active", "_ts": "2026-06-19T02:36:01.005639+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "_", "_ts": "2026-06-19T02:36:01.066287+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "form", "_ts": "2026-06-19T02:36:01.127761+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:01.187814+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:01.247939+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Research", "_ts": "2026-06-19T02:36:01.308448+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "ing", "_ts": "2026-06-19T02:36:01.368676+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " memory", "_ts": "2026-06-19T02:36:01.431936+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " and", "_ts": "2026-06-19T02:36:01.492339+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " tool", "_ts": "2026-06-19T02:36:01.553337+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " usage", "_ts": "2026-06-19T02:36:01.613676+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:01.674222+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:01.734100+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"content", "_ts": "2026-06-19T02:36:01.794784+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:01.854824+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:01.914891+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Research", "_ts": "2026-06-19T02:36:01.975652+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " long", "_ts": "2026-06-19T02:36:02.035759+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:36:02.095006+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "term", "_ts": "2026-06-19T02:36:02.155413+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "/", "_ts": "2026-06-19T02:36:02.216179+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "short", "_ts": "2026-06-19T02:36:02.276829+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:36:02.337248+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "term", "_ts": "2026-06-19T02:36:02.396453+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " memory", "_ts": "2026-06-19T02:36:02.456806+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " systems", "_ts": "2026-06-19T02:36:02.517730+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " (", "_ts": "2026-06-19T02:36:02.577334+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "R", "_ts": "2026-06-19T02:36:02.637898+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "AG", "_ts": "2026-06-19T02:36:02.697919+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " vs", "_ts": "2026-06-19T02:36:02.758813+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ".", "_ts": "2026-06-19T02:36:02.818734+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " fine", "_ts": "2026-06-19T02:36:02.879700+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:36:02.940122+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "tuning", "_ts": "2026-06-19T02:36:03.001968+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ")", "_ts": "2026-06-19T02:36:03.062639+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " and", "_ts": "2026-06-19T02:36:03.123126+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " tool", "_ts": "2026-06-19T02:36:03.184277+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "-", "_ts": "2026-06-19T02:36:03.244960+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "calling", "_ts": "2026-06-19T02:36:03.304857+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " mechanisms", "_ts": "2026-06-19T02:36:03.365939+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:03.426508+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:03.486348+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"status", "_ts": "2026-06-19T02:36:03.548936+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:03.616145+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:03.676432+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "pending", "_ts": "2026-06-19T02:36:03.736721+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:03.797342+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "},{", "_ts": "2026-06-19T02:36:03.857928+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"active", "_ts": "2026-06-19T02:36:03.919944+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "_", "_ts": "2026-06-19T02:36:03.981080+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "form", "_ts": "2026-06-19T02:36:04.041774+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:04.102845+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:04.163571+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Research", "_ts": "2026-06-19T02:36:04.224483+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "ing", "_ts": "2026-06-19T02:36:04.285387+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " orchestration", "_ts": "2026-06-19T02:36:04.348283+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " frameworks", "_ts": "2026-06-19T02:36:04.410096+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " and", "_ts": "2026-06-19T02:36:04.471032+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " production", "_ts": "2026-06-19T02:36:04.531823+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " deployment", "_ts": "2026-06-19T02:36:04.592960+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:04.653119+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:04.713617+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"content", "_ts": "2026-06-19T02:36:04.774331+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:04.834871+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:04.896163+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Research", "_ts": "2026-06-19T02:36:04.956953+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " current", "_ts": "2026-06-19T02:36:05.017875+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " orchestration", "_ts": "2026-06-19T02:36:05.078078+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " frameworks", "_ts": "2026-06-19T02:36:05.138588+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " (", "_ts": "2026-06-19T02:36:05.199375+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Lang", "_ts": "2026-06-19T02:36:05.259891+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Graph", "_ts": "2026-06-19T02:36:05.320693+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:05.381438+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Crew", "_ts": "2026-06-19T02:36:05.442290+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "AI", "_ts": "2026-06-19T02:36:05.503228+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:05.563856+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " Auto", "_ts": "2026-06-19T02:36:05.624356+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Gen", "_ts": "2026-06-19T02:36:05.686015+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ")", "_ts": "2026-06-19T02:36:05.748200+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " and", "_ts": "2026-06-19T02:36:05.809401+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " deployment", "_ts": "2026-06-19T02:36:05.869405+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "/", "_ts": "2026-06-19T02:36:05.929917+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "evaluation", "_ts": "2026-06-19T02:36:05.990998+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " strategies", "_ts": "2026-06-19T02:36:06.051971+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:06.112687+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:06.172981+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"status", "_ts": "2026-06-19T02:36:06.236725+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:06.297698+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:06.358273+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "pending", "_ts": "2026-06-19T02:36:06.419227+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:06.480658+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "},{", "_ts": "2026-06-19T02:36:06.541260+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"active", "_ts": "2026-06-19T02:36:06.601719+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "_", "_ts": "2026-06-19T02:36:06.663433+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "form", "_ts": "2026-06-19T02:36:06.723641+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:06.784706+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:06.845974+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Synt", "_ts": "2026-06-19T02:36:06.907817+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "hes", "_ts": "2026-06-19T02:36:06.968120+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "izing", "_ts": "2026-06-19T02:36:07.028742+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " findings", "_ts": "2026-06-19T02:36:07.088504+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " into", "_ts": "2026-06-19T02:36:07.151653+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " final", "_ts": "2026-06-19T02:36:07.212921+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " report", "_ts": "2026-06-19T02:36:07.274012+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:07.335766+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:07.397378+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"content", "_ts": "2026-06-19T02:36:07.457556+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:07.518444+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:07.579574+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Synt", "_ts": "2026-06-19T02:36:07.640395+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "hesize", "_ts": "2026-06-19T02:36:07.703912+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " all", "_ts": "2026-06-19T02:36:07.764446+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " research", "_ts": "2026-06-19T02:36:07.825645+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " notes", "_ts": "2026-06-19T02:36:07.886673+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " into", "_ts": "2026-06-19T02:36:07.949415+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " a", "_ts": "2026-06-19T02:36:08.013042+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " comprehensive", "_ts": "2026-06-19T02:36:08.074122+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " architecture", "_ts": "2026-06-19T02:36:08.135309+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " guide", "_ts": "2026-06-19T02:36:08.196410+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " in", "_ts": "2026-06-19T02:36:08.258987+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " /", "_ts": "2026-06-19T02:36:08.320041+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "workspace", "_ts": "2026-06-19T02:36:08.381845+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "/", "_ts": "2026-06-19T02:36:08.443530+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "report", "_ts": "2026-06-19T02:36:08.504778+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ".", "_ts": "2026-06-19T02:36:08.566367+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "md", "_ts": "2026-06-19T02:36:08.627657+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:08.688958+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": ",", "_ts": "2026-06-19T02:36:08.750972+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"status", "_ts": "2026-06-19T02:36:08.811701+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":", "_ts": "2026-06-19T02:36:08.873425+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:08.934977+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "pending", "_ts": "2026-06-19T02:36:08.995945+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"", "_ts": "2026-06-19T02:36:09.057261+00:00"}
{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "}]}", "_ts": "2026-06-19T02:36:09.120191+00:00"}
{"type": "tool_start", "tool_name": "write_todos", "args": "{\"todos\":[{\"active_form\":\"Planning research on Architecting Agentic AI Solutions\",\"content\":\"Create research plan for Architecting Agentic AI Solutions using a planner subagent\",\"status\":\"pending\"},{\"active_form\":\"Researching multi-agent architectures\",\"content\":\"Research multi-agent architectural patterns (e.g., hierarchical, joint, federated)\",\"status\":\"pending\"},{\"active_form\":\"Researching reasoning and planning patterns\",\"content\":\"Research reasoning patterns like ReAct, Chain-of-Thought, Reflection, and Tree-of-Thoughts\",\"status\":\"pending\"},{\"active_form\":\"Researching memory and tool usage\",\"content\":\"Research long-term/short-term memory systems (RAG vs. fine-tuning) and tool-calling mechanisms\",\"status\":\"pending\"},{\"active_form\":\"Researching orchestration frameworks and production deployment\",\"content\":\"Research current orchestration frameworks (LangGraph, CrewAI, AutoGen) and deployment/evaluation strategies\",\"status\":\"pending\"},{\"active_form\":\"Synthesizing findings into final report\",\"content\":\"Synthesize all research notes into a comprehensive architecture guide in /workspace/report.md\",\"status\":\"pending\"}]}", "_ts": "2026-06-19T02:36:09.248582+00:00"}
{"type": "todos_update", "todos": [{"active_form": "Planning research on Architecting Agentic AI Solutions", "content": "Create research plan for Architecting Agentic AI Solutions using a planner subagent", "status": "pending"}, {"active_form": "Researching multi-agent architectures", "content": "Research multi-agent architectural patterns (e.g., hierarchical, joint, federated)", "status": "pending"}, {"active_form": "Researching reasoning and planning patterns", "content": "Research reasoning patterns like ReAct, Chain-of-Thought, Reflection, and Tree-of-Thoughts", "status": "pending"}, {"active_form": "Researching memory and tool usage", "content": "Research long-term/short-term memory systems (RAG vs. fine-tuning) and tool-calling mechanisms", "status": "pending"}, {"active_form": "Researching orchestration frameworks and production deployment", "content": "Research current orchestration frameworks (LangGraph, CrewAI, AutoGen) and deployment/evaluation strategies", "status": "pending"}, {"active_form": "Synthesizing findings into final report", "content": "Synthesize all research notes into a comprehensive architecture guide in /workspace/report.md", "status": "pending"}], "_ts": "2026-06-19T02:36:09.248899+00:00"}
{"type": "tool_output", "tool_name": "write_todos", "output": "Updated 6 todos: 0 completed, 0 in progress, 6 pending", "_ts": "2026-06-19T02:36:09.250578+00:00"}
{"type": "middleware_event", "event": "tool_audit", "tool_name": "write_todos", "total_calls": 2, "tools_breakdown": {"task": 1, "write_todos": 1}, "_ts": "2026-06-19T02:36:09.250757+00:00"}
{"type": "status", "content": "Researching...", "_ts": "2026-06-19T02:36:09.251363+00:00"}
{"type": "tool_call_start", "tool_name": "task", "tool_call_id": "cHDyA0Y63lRLneDh3Nr7knW1GhUnaDCX", "_ts": "2026-06-19T02:36:13.590050+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"description", "_ts": "2026-06-19T02:36:13.654822+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\":", "_ts": "2026-06-19T02:36:13.719709+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:36:13.783029+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "Create", "_ts": "2026-06-19T02:36:13.845677+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " a", "_ts": "2026-06-19T02:36:13.909833+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " research", "_ts": "2026-06-19T02:36:13.972604+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " plan", "_ts": "2026-06-19T02:36:14.034057+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " for", "_ts": "2026-06-19T02:36:14.095357+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " '", "_ts": "2026-06-19T02:36:14.158838+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "Architect", "_ts": "2026-06-19T02:36:14.221752+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "ing", "_ts": "2026-06-19T02:36:14.283961+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Agent", "_ts": "2026-06-19T02:36:14.347300+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "ic", "_ts": "2026-06-19T02:36:14.409889+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " AI", "_ts": "2026-06-19T02:36:14.473248+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Solutions", "_ts": "2026-06-19T02:36:14.535634+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "'.", "_ts": "2026-06-19T02:36:14.598687+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Break", "_ts": "2026-06-19T02:36:14.661728+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " this", "_ts": "2026-06-19T02:36:14.724396+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " into", "_ts": "2026-06-19T02:36:14.787020+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " ", "_ts": "2026-06-19T02:36:14.849568+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "4", "_ts": "2026-06-19T02:36:14.913222+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:36:14.976322+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "5", "_ts": "2026-06-19T02:36:15.038661+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " focused", "_ts": "2026-06-19T02:36:15.101261+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " research", "_ts": "2026-06-19T02:36:15.163404+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " sub", "_ts": "2026-06-19T02:36:15.226187+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:36:15.288549+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "topics", "_ts": "2026-06-19T02:36:15.351335+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " (", "_ts": "2026-06-19T02:36:15.413758+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "e", "_ts": "2026-06-19T02:36:15.476320+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ".", "_ts": "2026-06-19T02:36:15.539229+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "g", "_ts": "2026-06-19T02:36:15.601530+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ".,", "_ts": "2026-06-19T02:36:15.664051+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " multi", "_ts": "2026-06-19T02:36:15.726111+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:36:15.788267+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "agent", "_ts": "2026-06-19T02:36:15.851004+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " architectures", "_ts": "2026-06-19T02:36:15.913770+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:36:15.976470+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " reasoning", "_ts": "2026-06-19T02:36:16.038261+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " patterns", "_ts": "2026-06-19T02:36:16.101138+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " like", "_ts": "2026-06-19T02:36:16.162343+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Re", "_ts": "2026-06-19T02:36:16.223146+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "Act", "_ts": "2026-06-19T02:36:16.283672+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "/", "_ts": "2026-06-19T02:36:16.345345+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "Reflection", "_ts": "2026-06-19T02:36:16.406376+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:36:16.467804+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " tool", "_ts": "2026-06-19T02:36:16.529217+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " use", "_ts": "2026-06-19T02:36:16.592311+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " &", "_ts": "2026-06-19T02:36:16.653110+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " memory", "_ts": "2026-06-19T02:36:16.714075+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:36:16.775275+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " orchestration", "_ts": "2026-06-19T02:36:16.835914+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " frameworks", "_ts": "2026-06-19T02:36:16.896839+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:36:16.959346+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " and", "_ts": "2026-06-19T02:36:17.020704+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " production", "_ts": "2026-06-19T02:36:17.082546+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " deployment", "_ts": "2026-06-19T02:36:17.149706+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "/", "_ts": "2026-06-19T02:36:17.211230+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "eval", "_ts": "2026-06-19T02:36:17.271396+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ").", "_ts": "2026-06-19T02:36:17.332942+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " Ask", "_ts": "2026-06-19T02:36:17.395899+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " the", "_ts": "2026-06-19T02:36:17.456880+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " user", "_ts": "2026-06-19T02:36:17.517311+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " if", "_ts": "2026-06-19T02:36:17.577714+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " they", "_ts": "2026-06-19T02:36:17.638977+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " want", "_ts": "2026-06-19T02:36:17.700832+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " a", "_ts": "2026-06-19T02:36:17.761877+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " technical", "_ts": "2026-06-19T02:36:17.823062+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " deep", "_ts": "2026-06-19T02:36:17.882908+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:36:17.943660+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "dive", "_ts": "2026-06-19T02:36:18.007478+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " or", "_ts": "2026-06-19T02:36:18.068402+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " a", "_ts": "2026-06-19T02:36:18.129892+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " high", "_ts": "2026-06-19T02:36:18.190909+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "-", "_ts": "2026-06-19T02:36:18.252065+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "level", "_ts": "2026-06-19T02:36:18.314232+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": " overview", "_ts": "2026-06-19T02:36:18.377331+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ".", "_ts": "2026-06-19T02:36:18.438489+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:36:18.499592+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": ",", "_ts": "2026-06-19T02:36:18.561843+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"sub", "_ts": "2026-06-19T02:36:18.623156+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "agent", "_ts": "2026-06-19T02:36:18.684048+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "_", "_ts": "2026-06-19T02:36:18.745584+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "type", "_ts": "2026-06-19T02:36:18.806683+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\":", "_ts": "2026-06-19T02:36:18.867727+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:36:18.929530+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "planner", "_ts": "2026-06-19T02:36:18.990436+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:36:19.051523+00:00"}
{"type": "tool_args_delta", "tool_name": "task", "args_delta": "}", "_ts": "2026-06-19T02:36:19.112041+00:00"}
{"type": "tool_start", "tool_name": "task", "args": "{\"description\":\"Create a research plan for 'Architecting Agentic AI Solutions'. Break this into 4-5 focused research sub-topics (e.g., multi-agent architectures, reasoning patterns like ReAct/Reflection, tool use & memory, orchestration frameworks, and production deployment/eval). Ask the user if they want a technical deep-dive or a high-level overview.\",\"subagent_type\":\"planner\"}", "_ts": "2026-06-19T02:36:19.237782+00:00"}
{"type": "status", "content": "Running planner subagent...", "_ts": "2026-06-19T02:36:19.238038+00:00"}
{"type": "tool_output", "tool_name": "task", "output": "Error executing task: AgentMemoryToolset 'deep-memory' defines a tool whose name conflicts with existing tool from AgentMemoryToolset 'deep-memory': 'read_memory'. Rename the tool or wrap the toolset in a `PrefixedToolset` to avoid name conflicts.", "_ts": "2026-06-19T02:36:19.243037+00:00"}
{"type": "middleware_event", "event": "tool_audit", "tool_name": "task", "total_calls": 3, "tools_breakdown": {"task": 2, "write_todos": 1}, "_ts": "2026-06-19T02:36:19.243154+00:00"}
{"type": "status", "content": "Researching...", "_ts": "2026-06-19T02:36:19.243431+00:00"}
{"type": "tool_call_start", "tool_name": "update_todo_status", "tool_call_id": "ZnZgOXQvit8nd2uYS9OdCpyxuR6xhIvy", "_ts": "2026-06-19T02:36:21.228630+00:00"}
{"type": "tool_args_delta", "tool_name": "update_todo_status", "args_delta": "\"status", "_ts": "2026-06-19T02:36:21.290893+00:00"}
{"type": "tool_args_delta", "tool_name": "update_todo_status", "args_delta": "\":", "_ts": "2026-06-19T02:36:21.352727+00:00"}
{"type": "tool_args_delta", "tool_name": "update_todo_status", "args_delta": "\"", "_ts": "2026-06-19T02:36:21.415528+00:00"}
{"type": "tool_args_delta", "tool_name": "update_todo_status", "args_delta": "in", "_ts": "2026-06-19T02:36:21.478715+00:00"}
{"type": "tool_args_delta", "tool_name": "update_todo_status", "args_delta": "_", "_ts": "2026-06-19T02:36:21.542488+00:00"}
{"type": "cancelled", "_ts": "2026-06-19T02:36:21.601370+00:00"}
{"type": "done", "_ts": "2026-06-19T02:36:21.601462+00:00"}
{"type": "session_created", "session_id": "49b65705-4300-4795-8b9a-306ce20018b1", "_ts": "2026-06-19T02:56:37.389896+00:00"}
{"type": "canvas_ready", "session_id": "49b65705-4300-4795-8b9a-306ce20018b1", "_ts": "2026-06-19T02:56:37.521105+00:00"}
{"type": "session_created", "session_id": "49b65705-4300-4795-8b9a-306ce20018b1", "_ts": "2026-06-19T02:56:38.207512+00:00"}
{"type": "canvas_ready", "session_id": "49b65705-4300-4795-8b9a-306ce20018b1", "_ts": "2026-06-19T02:56:38.208102+00:00"}
{"type": "background_task_completed", "task_id": "5d8c9577", "subagent_name": "general-purpose", "status": "completed", "description": "Research: Agentic Architectures & Multi-Agent Systems (MAS). Explore various organizational patterns for AI agents, including hierarchical, collaborative, and swarm models. Analyze key frameworks (e.g., AutoGen, CrewAI, LangGraph) and their architectural strengths. Search the web using Tavily/Jina, extract key findings from 3-5 authoritative sources, and write a detailed summary with source URLs to /workspace/notes/agentic_architectures.md.", "result_preview": "I have completed the research on agentic architectures and multi-agent systems (MAS).\n\n### Summary of Findings:\n- **Organizational Patterns**:\n - **Hierarchical/Orchestrator-Worker**: Best for long-horizon tasks requiring global planning and clear audit trails. It centralizes decision-making but introduces a single point of failure.\n - **Collaborative/Peer-to-Peer (Mesh)**: Offers high fault tolerance and scalability for parallel tasks through local decision rules, though it is harder to debug and interpret.\n - **Decentralized Swarm**: Inspired by biological systems, these provide extreme scalability and resilience but have very low inspectability.\n\n- **Framework Analysis**:\n - **AutoGen**: Optimized for asynchronous actor-style communication and rich structured logging. Ideal for complex coordination where auditability is key.\n - **CrewAI**: Focuses on \"Crews\" and \"Flows\" with enterprise-grade features like RBAC, secrets management, and checkpointing for resumable workflows.\n - **LangGraph**: Treats orchestration as a compiled StateGraph (DAG), offering fine-grained control over branching, parallelism, and state persistence with minimal token overhead.\n\nThe detailed summary, including source URLs and a selection guide, has been saved to `/workspace/notes/agentic_architectures.md`.", "error": null, "duration_seconds": 933.968316, "_ts": "2026-06-19T03:16:50.512649+00:00"}
{"type": "background_task_completed", "task_id": "696def3b", "subagent_name": "general-purpose", "status": "completed", "description": "Research: Orchestration & Workflow Design for Agentic AI. Focus on the mechanisms of task handoffs (e.g., routing, planning), guardrails (safety, output validation), human-in-the-loop (HITL) integration patterns, and stateful workflow management. Search the web using Tavily/Jina, extract key findings from 3-5 authoritative sources, and write a detailed summary with source URLs to /workspace/notes/orchestration_workflow.md.", "result_preview": "The research on **Orchestration & Workflow Design for Agentic AI** is complete. Key findings regarding task handoffs, guardrails, HITL, and state management have been synthesized into a detailed summary at `/workspace/notes/orchestration_workflow.md`.\n\n### Summary of Findings:\n- **Architectural Patterns**: A hybrid approach (Manager + Specialist Executors) is recommended to balance global control with modular scalability.\n- **Handoffs & Routing**: Intent classification combined with deterministic playbooks offers the best trade-off between predictability and complexity.\n- **State Management**: Durable workflow engines (e.g., Temporal, Argo) and event sourcing are critical for long-running tasks to ensure recovery and auditability.\n- **Human-in-the-Loop**: Risk-based approval gates and \"pause-and-wait\" patterns allow for safe human oversight without bottlenecking low-risk automated actions.\n- **Guardrails**: A multi-layered strategy\u2014including pre-generation policy gates, post-generation validators, and agent adapters\u2014ensures output quality and safety.\n\nThe summary includes citations to authoritative sources such as Microsoft's AutoGen documentation, research from arXiv, and technical blogs from Galileo and Confluent.", "error": null, "duration_seconds": 514.135289, "_ts": "2026-06-19T03:26:32.662970+00:00"}
{"type": "background_task_completed", "task_id": "30d0842a", "subagent_name": "general-purpose", "status": "completed", "description": "Research: Memory, State, and Context Management for Agentic AI. Analyze short-term (context window), mid-term (working memory/RAG), and long-term (persistent storage) memory systems. Explore techniques like recursive summarization, hierarchical retrieval, and stateful session management. Search the web using Tavily/Jina, extract key findings from 3-5 authoritative sources, and write a detailed summary with source URLs to /workspace/notes/memory_state.md.", "result_preview": "The research on Memory, State, and Context Management for Agentic AI is complete. \n\n### Summary of Findings:\n- **Short-Term Memory**: Focused on the active context window. Techniques include sliding windows, token pruning (to manage KV-cache costs), and \"late chunking\" to maintain semantic integrity.\n- **Mid-Term Memory**: Primarily handled via Retrieval-Augmented Generation (RAG). Key strategies involve hybrid retrieval (lexical + semantic), hierarchical retrieval pipelines (coarse-to-fine) to reduce noise, and re-ranking for better intent alignment.\n- **Long-Term Memory**: Utilizes persistent storage such as Knowledge Graphs for typed relationships and relational databases (like PostgreSQL with `pgvector`) for transactional integrity. Checkpointing and recursive summarization are vital for stateful session management.\n\n### Key Deliverable:\nThe detailed summary, including architectural patterns (Small, Medium, Large), failure modes, and a comprehensive list of authoritative sources, has been written to:\n`/workspace/notes/memory_state.md`", "error": null, "duration_seconds": 663.296615, "_ts": "2026-06-19T03:38:41.913492+00:00"}
{"type": "background_task_completed", "task_id": "0395872f", "subagent_name": "general-purpose", "status": "completed", "description": "Research: Real-World Deployment & Scaling Challenges for Agentic AI. Investigate the transition from Proof of Concept (POC) to production, focusing on reliability bottlenecks (e.g., hallucination loops, non-deterministic behavior), cost management (token usage optimization, model routing), and latency/concurrency issues. Analyze multi-agent scaling strategies and observability requirements in production environments. Search the web using Tavily/Jina, extract key findings from 3-5 authoritative sources, and write a detailed summary with source URLs to /workspace/notes/deployment_scaling.md.", "result_preview": "The research on real-world deployment and scaling challenges for agentic AI is complete. \n\nI have identified key production bottlenecks including hallucination loops, non-deterministic drift, and runaway token costs. I also analyzed mitigation strategies such as model routing, verifier agents, semantic caching, and hierarchical orchestration for multi-agent systems.\n\nThe detailed summary, including technical findings and source URLs, has been written to `/workspace/notes/deployment_scaling.md`.", "error": null, "duration_seconds": 760.435694, "_ts": "2026-06-19T03:52:32.160377+00:00"}
{"type": "background_task_completed", "task_id": "07b7ebad", "subagent_name": "general-purpose", "status": "completed", "description": "Research: Evaluation Frameworks & Governance for Agentic AI. Identify KPIs for agent reliability, accuracy, and safety (e.g., success rate, cost per task, hallucination rate). Explore common evaluation methodologies like LLM-as-a-judge, human-in-the-loop verification, and automated testing suites (e.g., AgentBench, GAIA). Analyze governance requirements including data privacy, auditability, and safety guardrails for production AI systems. Search the web using Tavily/Jina, extract key findings from 3-5 authoritative sources, and write a detailed summary with source URLs to /workspace/notes/evaluation_governance.md.", "result_preview": "I have completed the research on **Evaluation Frameworks & Governance for Agentic AI**. \n\nKey findings include:\n- **KPIs**: Identified core metrics including Task Success Rate, Cost Per Successful Task (CPST), Hallucination/Error Rates, and Latency targets.\n- **Methodologies**: Explored LLM-as-a-Judge (with calibration), Human-in-the-Loop oversight, and automated harnesses using benchmarks like AgentBench and GAIA.\n- **Governance**: Analyzed requirements for privacy-preserving telemetry, immutable audit logs, least-privilege scoping, and alignment with NIST AI RMF and ISO 42001 standards.\n\nThe detailed summary is available at: `/workspace/notes/evaluation_governance.md`.", "error": null, "duration_seconds": 442.28271, "_ts": "2026-06-19T04:01:06.232940+00:00"}
workspaces/49b65705-4300-4795-8b9a-306ce20018b1/history.json
+1
View File
@@ -0,0 +1 @@
[{"parts":[{"content":"research on architect agentic ai solution.","timestamp":"2026-06-19T02:36:21.581924Z","part_kind":"user-prompt"}],"timestamp":null,"instructions":null,"kind":"request","run_id":null,"conversation_id":null,"metadata":null}]
workspaces/49b65705-4300-4795-8b9a-306ce20018b1/meta.json
+1
View File
@@ -0,0 +1 @@
{"session_id": "49b65705-4300-4795-8b9a-306ce20018b1", "created_at": "2026-06-19T02:34:58.212200+00:00", "updated_at": "2026-06-19T02:36:21.601089+00:00", "title": "research on architect agentic ai solution.", "message_count": 1, "todos": [{"active_form": "Planning research on Architecting Agentic AI Solutions", "content": "Create research plan for Architecting Agentic AI Solutions using a planner subagent", "status": "pending"}, {"active_form": "Researching multi-agent architectures", "content": "Research multi-agent architectural patterns (e.g., hierarchical, joint, federated)", "status": "pending"}, {"active_form": "Researching reasoning and planning patterns", "content": "Research reasoning patterns like ReAct, Chain-of-Thought, Reflection, and Tree-of-Thoughts", "status": "pending"}, {"active_form": "Researching memory and tool usage", "content": "Research long-term/short-term memory systems (RAG vs. fine-tuning) and tool-calling mechanisms", "status": "pending"}, {"active_form": "Researching orchestration frameworks and production deployment", "content": "Research current orchestration frameworks (LangGraph, CrewAI, AutoGen) and deployment/evaluation strategies", "status": "pending"}, {"active_form": "Synthesizing findings into final report", "content": "Synthesize all research notes into a comprehensive architecture guide in /workspace/report.md", "status": "pending"}]}
workspaces/49b65705-4300-4795-8b9a-306ce20018b1/workspace/DEEP.md
+11
View File
@@ -0,0 +1,11 @@
# DeepResearch Workspace
## Research Workflow
1. Save research notes to `/workspace/notes/` (one file per sub-topic)
2. Track all sources with URLs in notes
3. Write the final report to `/workspace/report.md`
## File Organization
- `/workspace/notes/` — Research notes per topic
- `/workspace/report.md` — Final research report
- `/workspace/sources.md` — Source tracking (optional)
workspaces/49b65705-4300-4795-8b9a-306ce20018b1/workspace/MEMORY.md
+3
View File
@@ -0,0 +1,3 @@
# Agent Memory
Persistent notes and context that carry across sessions.
workspaces/51dda135-b56a-49f0-973f-9814d895398c/events.jsonl
+5735
View File
File diff suppressed because one or more lines are too long
workspaces/51dda135-b56a-49f0-973f-9814d895398c/history.json
+1
View File
File diff suppressed because one or more lines are too long
workspaces/51dda135-b56a-49f0-973f-9814d895398c/meta.json
+1
View File
@@ -0,0 +1 @@
{"session_id": "51dda135-b56a-49f0-973f-9814d895398c", "created_at": "2026-06-19T02:56:46.244807+00:00", "updated_at": "2026-06-19T04:07:11.772733+00:00", "title": "research on architect agentic ai solution.", "message_count": 82, "todos": [{"active_form": "Researching Agentic Architectures & Multi-Agent Systems (MAS)", "content": "Research sub-topic 1: Agentic Architectures & Multi-Agent Systems (MAS)", "status": "pending"}, {"active_form": "Researching Orchestration & Workflow Design", "content": "Research sub-topic 2: Orchestration & Workflow Design", "status": "pending"}, {"active_form": "Researching Memory, State, and Context Management", "content": "Research sub-topic 3: Memory, State, and Context Management", "status": "pending"}, {"active_form": "Researching Real-World Deployment & Scaling Challenges", "content": "Research sub-topic 4: Real-World Deployment & Scaling Challenges", "status": "pending"}, {"active_form": "Researching Evaluation Frameworks & Governance", "content": "Research sub-topic 5: Evaluation Frameworks & Governance", "status": "pending"}, {"active_form": "Synthesizing final research report", "content": "Synthesize findings into final research report", "status": "pending"}]}
workspaces/51dda135-b56a-49f0-973f-9814d895398c/workspace/DEEP.md
+11
View File
@@ -0,0 +1,11 @@
# DeepResearch Workspace
## Research Workflow
1. Save research notes to `/workspace/notes/` (one file per sub-topic)
2. Track all sources with URLs in notes
3. Write the final report to `/workspace/report.md`
## File Organization
- `/workspace/notes/` — Research notes per topic
- `/workspace/report.md` — Final research report
- `/workspace/sources.md` — Source tracking (optional)
workspaces/51dda135-b56a-49f0-973f-9814d895398c/workspace/MEMORY.md
+3
View File
@@ -0,0 +1,3 @@
# Agent Memory
Persistent notes and context that carry across sessions.
workspaces/51dda135-b56a-49f0-973f-9814d895398c/workspace/report.md
+157
View File
@@ -0,0 +1,157 @@
# Architecting Agentic AI Solutions: A Comprehensive Guide
## Executive Summary
The shift from monolithic LLM applications to agentic AI systems represents a fundamental change in software architecture. Instead of linear prompts, agentic solutions rely on dynamic reasoning, multi-agent coordination, and persistent state management. This report provides a deep dive into the core pillars of architecting these systems: organizational patterns for multi-agent systems (MAS), sophisticated orchestration and workflow design, durable memory structures, real-world deployment challenges, and rigorous evaluation frameworks. The primary takeaway for architects is the move toward **Hybrid Orchestration**—balancing the predictability of deterministic workflows with the flexibility of autonomous agentic reasoning.
## 1. Agentic Architectures & Multi-Agent Systems (MAS)
Architecting a multi-agent system requires choosing an organizational pattern that aligns with the complexity and reliability requirements of the task.
### 1.1 Organizational Patterns
* **Hierarchical / Orchestrator-Worker**: A centralized "Manager" or "Planner" decomposes high-level goals into subtasks, delegating them to specialized "Workers."
* *Strengths*: High auditability, clear decision flows, and simplified global optimization.
* *Weaknesses*: The orchestrator becomes a single point of failure and a potential performance bottleneck.
* **Collaborative / Peer-to-Peer (Mesh)**: Agents communicate directly with neighbors without a central coordinator.
* *Strengths*: High fault tolerance and massive scalability for parallelizable tasks.
* *Weaknesses*: Difficult to debug; requires complex local heuristics to prevent emergent miscoordination.
* **Decentralized Swarm**: Inspired by biological systems, agents follow simple local rules to achieve global objectives (e.g., swarm robotics).
* *Strengths*: Extreme resilience and adaptability.
* *Weaknesses*: Very low inspectability and high difficulty in guaranteeing specific outcomes.
### 1.2 Key Framework Analysis
Architects often choose between three leading frameworks based on these needs:
* **AutoGen (Microsoft)**: Best for **complex coordination**. It uses an actor-based, message-driven model that supports asynchronous communication and rich audit trails via OpenTelemetry.
* **CrewAI**: Best for **enterprise automation**. It focuses on "Crews" with predefined roles and "Flows," offering built-in RBAC (Role-Based Access Control), secrets management, and robust checkpointing for resumable workflows.
* **LangGraph (LangChain)**: Best for **high-throughput production pipelines**. It treats orchestration as a compiled StateGraph (DAG), providing fine-grained control over branching, cycles, and state persistence with minimal token overhead.
## 2. Orchestration & Workflow Design
Orchestration is the "nervous system" of an agentic solution, defining how intent is translated into action.
### 2.1 Routing Mechanisms
To ensure reliability, architects should move away from purely autonomous routing toward **Hybrid Routing**:
* **Intent Classification**: Use a fast, cheap model (e.g., GPT-4o-mini or Haiku) to classify user intent.
* **Deterministic Playbooks**: Once intent is classified, route the request through a pre-defined playbook to ensure predictable behavior for common tasks.
* **Learned Routers**: Use LLM-based routers only for complex, low-frequency requests where a playbook cannot be easily defined.
### 2.2 Stateful Workflow Management
Long-running agents require "memory" of the process itself:
* **Durable State**: Use engines like **Temporal** or **Argo** to manage long-lived workflows. These provide checkpointing, allowing agents to resume from a failed step without restarting the entire sequence.
* **Event Sourcing**: Maintain an immutable "Agent Decision Record" (ADR). This allows for state-based replay, making it significantly easier to debug why an agent made a specific choice at a specific time.
### 2.3 Human-in-the-Loop (HITL)
Human oversight should be risk-based:
* **Asynchronous Oversight**: For low-risk tasks, use Slack/Email for approval.
* **Interactive "Pause-and-Wait"**: For high-stakes actions (e.g., database deletes, financial transfers), the agent must reach a "checkpoint" and block until a human provides an idempotency key or signature.
### 2.4 Guardrails & Output Validation
A multi-layered defense strategy is required:
* **Pre-generation**: Policy gates to check if the requested action is allowed.
* **Post-generation**: Automated verifiers (using "LLM-as-a-Judge" or regex) to ensure the output matches the required schema.
* **Agent Adapters**: Standardized interfaces that enforce contract checks between agents and external tools.
## 3. Memory, State, and Context Management
Agents must manage information across three temporal scales to remain effective and cost-efficient.
### 3.1 The Memory Hierarchy
* **Short-Term (Working Memory)**: Managed via the context window. Techniques like **Token Pruning** and **Sliding Windows** are essential to keep the KV-cache manageable while preserving task-critical tokens.
* **Mid-Term (Knowledge Grounding)**: Handled via **Retrieval-Augmented Generation (RAG)**. Architects should prioritize **Hierarchical Retrieval**—selecting high-level summaries before drilling into specific chunks—to reduce noise and "retrieval thrash."
* **Long-Term (Persistent Storage)**: For facts and skills, use a combination of **Knowledge Graphs (KG)** for typed relationships and **Relational Stores (PostgreSQL + pgvector)** for ACID-compliant semantic search.
### 3.2 Architecture Trade-offs
| Architecture | Components | Best Use Case |
| :--- | :--- | :--- |
| **Small (Prototype)** | Local LLM + `pgvector` + Chroma | Rapid development, low cost |
| **Medium (Production)** | Orchestrator + Qdrant + Relational Store + KG | Balanced latency and recall |
| **Large (Enterprise)** | Distributed Vector Index + Neo4j + Hierarchical RAG | Billion-scale data, strict governance |
## 4. Real-World Deployment & Scaling Challenges
Moving from a POC to production reveals the "Agentic Tax"—the complexity of managing non-deterministic systems at scale.
### 4.1 Reliability Bottlenecks
* **Reasoning Cascades**: Agents can fall into "hallucination loops" where an error in step 1 becomes the foundation for step 2. Use **Verifier Agents** (pure-function agents that only check inputs/outputs) to break these loops.
* **Non-determinism & Drift**: Context drift over long workflows makes debugging difficult. Implement **OpenTelemetry** to trace every agent span and model decision.
### 4.2 Cost & Latency Optimization
* **Model Routing**: Route simple tasks (classification, extraction) to smaller models; reserve "reasoning" models for planning.
* **Semantic Caching**: Cache common responses to save tokens and reduce latency.
* **Pipelining**: Stream outputs to the user while background agents continue synthesizing subsequent steps.
### 4.3 Observability & Operations (LLMOps)
* **KPIs**: Track **Task Success Rate**, **Cost Per Successful Task (CPST)**, and **Hallucination/Error Rates**.
* **Continuous Evaluation**: Implement real-time alerting on token burn, latency percentiles, and "trace-grounded" hallucination scores.
## 5. Evaluation Frameworks & Governance
You cannot manage what you cannot measure. A production agentic system requires a rigorous evaluation suite.
### 5.1 Multi-Layered Evaluation
* **Automated Benchmarks**: Utilize harnesses like **AgentBench**, **GAIA**, and **SWE-bench** to establish a performance baseline.
* **LLM-as-a-Judge**: Use high-capability models to evaluate outputs against explicit rubrics. To avoid positional bias, use randomized presentation and cross-model judging.
* **Adversarial Testing**: Conduct "red-teaming" to identify prompt injection vulnerabilities and execution hijacking risks.
### 5.2 Governance & Compliance
To meet enterprise standards (NIST AI RMF, ISO 42001):
* **Immutable Audit Logs**: Capture every input, output, context chunk, and tool call.
* **Least-Privilege Scoping**: Agents should only have access to the specific resources required for their role.
* **Privacy-Preserving Telemetry**: Ensure logs are pseudonymized before being sent to monitoring tools.
## Conclusions and Future Outlook
Architecting agentic AI is less about "building an agent" and more about building a **robust infrastructure for agents**. The winners in this space will be those who move beyond simple prompt engineering to build durable, stateful systems that prioritize:
1. **Predictability via Orchestration**: Using playbooks and intent routing to constrain the search space.
2. **Reliability via Verification**: Implementing multi-layered guardrails and verifier agents.
3. **Scalability via Memory Hierarchy**: Combining RAG, Knowledge Graphs, and durable workflow engines.
As models become more capable, the bottleneck will shift from "what can the model do" to "how safely and efficiently can we coordinate its actions at scale."
## References
[1] https://microsoft.github.io/autogen/stable//user-guide/core-user-guide/index.html
[2] https://docs.crewai.com/en/introduction
[3] https://langchain.com/blog/langgraph-cloud
[4] https://c-sharpcorner.com/article/llm-agent-orchestration-patterns-architectural-frameworks-for-managing-complex
[5] https://galileo.ai/blog/architectures-for-multi-agent-systems
[6] https://blog.gopenai.com/intent-routing-for-ai-agents-e075d64da6c9
[7] https://aclanthology.org/2024.insights-1.15.pdf
[8] https://confluent.io/blog/compliant-ai-agents-stateful-stream-processing
[9] https://fast.io/resources/argo-workflows-ai-agents
[10] https://linkedin.com/posts/anumohan-mohanan-sudha-148833b_langgraph-langchain-llm-activity-7385133375763800065-Oez2
[11] https://docs.aws.amazon.com/wellarchitected/latest/agentic-ai-lens/agentperf03-bp01.html
[12] https://machinelearningmastery.com/7-steps-to-mastering-memory-in-agentic-ai-systems
[13] https://arxiv.org/html/2501.09136v3
[14] https://milvus.io/ai-quick-reference/what-are-the-differences-between-exact-and-approximate-vector-search
[15] https://developer.nvidia.com/blog/enhancing-rag-pipelines-with-re-ranking
[16] https://tigerdata.com/learn/building-ai-agents-with-persistent-memory-a-unified-database-approach
[17] https://puppygraph.com/blog/knowledge-graph-memory
[18] https://eunomia.dev/blog/2025/05/11/checkpoint-restore-systems-evolution-techniques-and-applications-in-ai-agents
[19] https://air-governance-framework.finos.org/mitigations/mi-14_encryption-of-ai-data-at-rest.html
[20] https://community.databricks.com/t5/technical-blog/building-intelligent-ai-agents-the-complete-guide-from-blueprint/ba-p/115708
[21] https://mem0.ai/blog/how-to-add-memory-to-autonomous-ai-agents
[22] https://snorkel.ai/blog/retrieval-augmented-generation-rag-failure-modes-and-how-to-fix-them
[23] https://dev.to/kuldeep_paul/ten-failure-modes-of-rag-nobody-talks-about-and-how-to-detect-them-systematically-7i4
[24] https://galileo.ai/blog/metrics-first-approach-to-llm-evaluation
[25] https://deepeval.com/docs/metrics-introduction
[26] https://arxiv.org/html/2405.08944v1
[27] https://arunangshudas.com/blog/ai-agent/memory-for-agents-vector-kv-graph
[28] https://dac.digital/ai-hallucination-risks-how-to-spot-and-prevent
[29] https://morphllm.com/llm-cost-optimization
[30] https://blaxel.ai/blog/sub-second-sandbox-startup-time
[31] https://daily.dev/blog/ai-agents-guide-for-developers-langchain-crewai
[32] https://arxiv.org/html/2512.08769v1
[33] https://alessandropignati.substack.com/p/the-9-second-post-mortem-when-your
[34] https://reddit.com/r/AutoGPT/comments/1q9ktik/i_stopped_my_autogpt_agents_from_burning_50hour
[35] https://giskard.ai/knowledge/function-calling-in-llms-testing-agent-tool-usage-for-ai-security
[36] https://tianpan.co/blog/2025-11-03-llm-routing-model-cascades
[37] https://galileo.ai/blog/hidden-cost-of-agentic-ai
[38] https://kunalganglani.com/blog/llm-api-latency-benchmarks-2026
[39] https://aviso.com/blog/how-to-evaluate-ai-agents-latency-cost-safety-roi
[40] https://mirantis.com/blog/llm-optimization-techniques
[41] https://zenml.io/llmops-database/scaling-multi-agent-autonomous-coding-systems
[42] https://gurusup.com/blog/multi-agent-orchestration
[43] https://arxiv.org/html/2601.13671v1
[44] https://victoriametrics.com/blog/ai-agents-observability
[45] https://opentelemetry.io/blog/2025/ai-agent-observability
[46] https://braintrust.dev/articles/what-is-llm-monitoring
[47] https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-serverless/grounding-and-rag.html
[48] https://codenotary.com/blog/when-ai-goes-rogue-the-replit-incident-and-its-lessons
[49] https://aws.amazon.com/blogs/machine-learning/best-practices-for-building-robust-generative-ai-applications-with-amazon-bedrock-agents-part-2
[50] https://arxiv.org/html/2602.16666v1
[51] https://datarobot.com/blog/cut-agentic-ai-development-costs
[52] https://techcrunch.com/2024/12/13/openai-blames-its-massive-chatgpt-outage-on-a-new-telemetry-service
[53] https://cerias.purdue.edu/research/projects/home/detail/411/causalitydriven_mitigation_of_cascading_failures_in_distributed_systems
workspaces/763605a6-5162-4912-b66a-a4e32cb16cd2/events.jsonl
+1818
View File
File diff suppressed because it is too large Load Diff
workspaces/763605a6-5162-4912-b66a-a4e32cb16cd2/history.json
+1
View File
File diff suppressed because one or more lines are too long
workspaces/763605a6-5162-4912-b66a-a4e32cb16cd2/meta.json
+1
View File
@@ -0,0 +1 @@
{"session_id": "763605a6-5162-4912-b66a-a4e32cb16cd2", "created_at": "2026-06-19T02:22:18.746464+00:00", "updated_at": "2026-06-19T02:24:51.419506+00:00", "title": "research on architect agentic ai solution.", "message_count": 24, "todos": [{"active_form": "Planning research on architecting agentic AI solutions", "content": "Create a detailed research plan for architecting agentic AI solutions", "status": "in_progress"}, {"active_form": "Researching multi-agent orchestration patterns", "content": "Research multi-agent orchestration patterns (e.g., Router, Sequential, Hierarchical, Joint-Collaboration)", "status": "pending"}, {"active_form": "Researching memory architectures", "content": "Research memory architectures for agents (short-term vs long-term, RAG vs fine-tuning vs context window management)", "status": "pending"}, {"active_form": "Researching tool use and planning strategies", "content": "Research advanced planning techniques (CoT, Tree of Thoughts, ReAct) and tool-calling mechanisms", "status": "pending"}, {"active_form": "Researching evaluation frameworks", "content": "Research evaluation frameworks for agentic systems (benchmarks, human-in-the-loop, automated testing)", "status": "pending"}, {"active_form": "Researching real-world deployment and production challenges", "content": "Research production challenges including latency, cost management, reliability, and security in agentic workflows", "status": "pending"}, {"active_form": "Synthesizing findings into a final report", "content": "Synthesize all research notes into a comprehensive architectural guide in /workspace/report.md", "status": "pending"}]}
workspaces/763605a6-5162-4912-b66a-a4e32cb16cd2/workspace/DEEP.md
+11
View File
@@ -0,0 +1,11 @@
# DeepResearch Workspace
## Research Workflow
1. Save research notes to `/workspace/notes/` (one file per sub-topic)
2. Track all sources with URLs in notes
3. Write the final report to `/workspace/report.md`
## File Organization
- `/workspace/notes/` — Research notes per topic
- `/workspace/report.md` — Final research report
- `/workspace/sources.md` — Source tracking (optional)
workspaces/763605a6-5162-4912-b66a-a4e32cb16cd2/workspace/MEMORY.md
+3
View File
@@ -0,0 +1,3 @@
# Agent Memory
Persistent notes and context that carry across sessions.