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WFGY/ProblemMap/GlobalFixMap/Agents_Orchestration/autogen.md

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AutoGen: Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of Agents & Orchestration.
To reorient, go back here:

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Use this page when your orchestration uses AutoGen (ConversableAgent, GroupChat, function tools) and you see tool loops, wrong snippets, role mixing, or answers that flip between runs. The table maps symptoms to exact WFGY fix pages and gives a minimal recipe you can paste.

Acceptance targets

  • ΔS(question, retrieved) ≤ 0.45
  • Coverage ≥ 0.70 to the intended section or record
  • λ stays convergent across 3 paraphrases and 2 seeds
  • E_resonance stays flat on long windows

Open these first

Typical breakpoints and the right fix

  • Function tool calls wait on each other or retry forever
    Fix: lock roles, add timeouts, and echo a strict JSON schema in every tool response.
    Open: Multi-Agent Problems · Logic Collapse

  • Two agents overwrite the same memory namespace
    Fix: stamp mem_rev and mem_hash, split read and write, forbid cross section reuse.
    Open: role-drift · memory-overwrite

  • High similarity yet wrong meaning
    Fix: metric or index mismatch, or mixed write and read embeddings.
    Open: Embedding ≠ Semantic

  • Hybrid stack worse than a single retriever
    Fix: lock two stage query and add a deterministic reranker.
    Open: Query Parsing Split · Rerankers

  • Facts exist in the store yet never show up
    Fix: fragmentation or sharding misalignment.
    Open: Vectorstore Fragmentation

  • Citations inconsistent across agents or steps
    Fix: require cite then explain and lock snippet fields.
    Open: Retrieval Traceability · Data Contracts

  • Long GroupChat runs change style and logic
    Fix: split the plan and rejoin with a BBCR bridge.
    Open: Context Drift · Entropy Collapse

Fix in 60 seconds

  1. Measure ΔS
    Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
    Stable < 0.40, transitional 0.40 to 0.60, risk ≥ 0.60.

  2. Probe λ_observe
    Do a k sweep in retrieval (5, 10, 20). If ΔS stays high and flat, suspect metric or index mismatch.
    Reorder prompt headers. If λ flips, lock the schema and clamp variance with BBAM.

  3. Apply the module

  1. Verify
    Coverage ≥ 0.70. ΔS ≤ 0.45. Three paraphrases and two seeds with λ convergent.

Minimal AutoGen topology with WFGY checks

# Pseudocode: focus on control points you must keep
from autogen import ConversableAgent, GroupChat, GroupChatManager

user = ConversableAgent("user", system_message="task only")
retriever = ConversableAgent("retriever", tools=[search_tool])
reasoner = ConversableAgent("reasoner", tools=[rag_tool])
auditor = ConversableAgent("auditor", system_message="cite-then-explain, schema-locked")

group = GroupChat(
    agents=[user, retriever, reasoner, auditor],
    messages=[],
    max_round=8,
    speaker_selection_method="auto"
)
manager = GroupChatManager(groupchat=group)

# guardrails to add around the loop:
# 1) every tool result must echo the JSON schema
# 2) each step writes {snippet_id, section_id, source_url, offsets, tokens}
# 3) after generation run WFGY checks and stop if ΔS ≥ 0.60 or λ divergent

What this enforces

  • Tools return strict JSON and echo the schema. Free text cannot pollute arguments.
  • Snippet fields are fixed and citations come first.
  • Post generation WFGY checks can halt the run when ΔS is high or λ flips.

Specs and recipes RAG Architecture & Recovery · Retrieval Playbook · Retrieval Traceability · Data Contracts

AutoGen specific gotchas

  • Function schema is too loose and allows mixed JSON and free text. Lock parameters and echo schema on every call. See Prompt Injection

  • GroupChat runs are too long and entropy rises. Split the plan and rejoin with a BBCR bridge. See Context Drift

  • Shared memory overwrites between agents. Add mem_rev and mem_hash, forbid cross section reuse. See memory-overwrite

  • Retrieval and rerank are inconsistent across agents. Unify analyzer and metric or add a reranker. See Rerankers

When to escalate

  • ΔS remains ≥ 0.60 Rebuild the index using the checklists and verify with a small gold set. Retrieval Playbook

  • Identical input yields different answers across runs Check version skew and session state. Pre-Deploy Collapse

🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS) TXTOS.txt 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

Explore More

Layer Page What it’s for
⭐ Proof WFGY Recognition Map External citations, integrations, and ecosystem proof
⚙️ Engine WFGY 1.0 Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map Problem Map 2.0 Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
🧰 App TXT OS .txt semantic OS with fast bootstrap
🧰 App Blah Blah Blah Abstract and paradox Q&A built on TXT OS
🧰 App Blur Blur Blur Text to image generation with semantic control
🏡 Onboarding Starter Village Guided entry point for new users

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