🤝 Agent Consensus Protocols

March 6, 2026 · View on GitHub

Make two (or ten) LLMs reach a decision on time and on budget — every run.

Why this page?
“Let them debate” sounds cool until:

the Critic never stops nit-picking,

the Planner forgets half the context,

the Coder ships five conflicting drafts, and

your bill looks like a crypto rug-pull.

WFGY adds measurable semantic tension (ΔS) and logic vectors (λ) to debate & QA loops, so you can prove consensus is reached — not guessed.

1 · Three Canonical Consensus Modes

Mode	Use Case	# Agents	Target ΔS	Target λ
Critic-Coder	Code/SQL generation with review	2	≤ 0.40	convergent
Triad Debate	Fact-check or legal reasoning	3	≤ 0.45	convergent
Crew Vote	4 – 10 micro-agents on long plan	N	≤ 0.50	majority convergent

ΔS ceiling rises gently with group size; λ must return to convergent after each round.

2 · Failure Modes (Why Votes Stall)

#	Failure	ΔS / λ Symptom	Example
1	Infinite Debate	λ oscillates (→, ←, →…)	Critic re-opens same defect
2	Topic Drift	ΔS > 0.60 vs. original prompt	Agents add new, unrelated goals
3	Split Brain	Two stable but divergent λ	50 / 50 vote, no tie-breaker
4	Early Merge	ΔS still 0.55 but λ forced convergent	“Agree to disagree” w/ wrong answer
5	Token Exhaust	E_resonance ↑ while ΔS unchanged	Loop burns context without progress

3 · WFGY Consensus Blueprint

Four guard layers; plug into any framework (AutoGen, LangChain, custom asyncio).

Layer	Module	Guard	Effect
Round ΔS Checker	BBMC	ΔS(agent, goal) ≤ ceiling	Drop off-topic vote
λ Mediator	ΔS + λ	λ must converge within 3 rounds	Force re-focus or escalate
Vote Auditor	WAI	JSON vote schema + arg defaults	No partial / malformed votes
BBCR Fallback	BBCR	> 5 rounds OR ΔS stagnates	Collapse & request human tie-break

flowchart TD
    subgraph Round
      Q[User Goal]
      A1[Agent 1]
      A2[Agent 2]
      A3[Agent 3]
      Q --> A1 --> V1[Vote JSON]
      Q --> A2 --> V2
      Q --> A3 --> V3
    end
    BBMC -->|ΔS filter| V1
    BBMC --> V2
    BBMC --> V3
    V1 & V2 & V3 --> Mediator[λ Mediator] -->|convergent?| Decision
    Mediator -.->|no| BBCR

4 · Practical Setup (AutoGen example)

from autogen import AssistantAgent, UserProxyAgent
from wfgy import consensus_filter

critic = AssistantAgent(name="critic")
coder  = AssistantAgent(name="coder")
user   = UserProxyAgent("user")

def callback(messages, state):
    # 1. measure ΔS + λ
    ok = consensus_filter(messages, ceiling=0.45, rounds=3)
    if not ok:
        return "STOP_DEBATE"
    return "CONTINUE"

coder.register_reply(callback)
critic.register_reply(callback)

15 lines → loop stops automatically when convergence proven or impossible.

5 · Debug Walk-Through

Log votes

print(state.votes)   # {critic: "reject, ΔS=0.48", coder: "accept, ΔS=0.41"}

Inspect λ trend

λ sequence: → → ← →  (oscillating)  ❌

Two divergent rounds trigger Mediator → requests narrowing question.

Trigger BBCR

ΔS stagnates at 0.52 for 3 rounds → BBCR => "Need human tie-break"

6 · Best-Practice Table

Tip	Why
Keep each vote to max 300 tokens.	Reduces E_resonance; easier ΔS calc
Pin goal & constraints in every round.	Prevents silent prompt drift
Summarise before vote.	Normalises embeddings → fair ΔS
Record vote reason (1-2 lines).	Faster root-cause when split-brain

🔗 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

If this repository helped, starring it improves discovery so more builders can find the docs and tools.