Multi-Agent Adversarial Debate Pipeline — Claude Workflow Catalog
Multi-Agent Adversarial Debate Pipeline
An 8-phase multi-agent pipeline that uses structured adversarial debate to produce high-quality, well-researched outputs. Combines blind deliberation, web search diversity, confidence-weighted synthesis, and independent verification to defend against sycophancy, consensus inertia, and error cascade.
researchaidebatemulti-agentresearch
Paste directly into Claude — best for one-off tasks and quick sessions
Download as a Markdown file — save to your project folder so Claude always has this workflow as context
Download as JSON — useful for importing into other tools or backing up your workflows
526
total tokens across 2 prompts
Estimated cost: $0.0016
Token counts approximate (OpenAI cl100k_base encoding, within ~15% of Claude). Pricing last verified 2026-04-12.
Pipeline
Failure Mode Coverage
Phase \ Failure
Sycophancy
Majority Tyranny
Bluster Effect
Error Cascade
Context Drift
Consensus Inertia
No New Signal
Role Bleed
Complexity Creep
Decision Oscillation
Coordinator Hedging
Phase 1: Input Preparation
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Phases
Phase 1
Input Preparation
Compose clean context for this round
Three components assembled: (1) Original constraints -- verbatim, never summarized. (2) Current synthesis -- compressed into Decided / Active / Minority Report. (3) Role-specific instruction with toke…
Defends: Context drift, Consensus inertia
If compressed context unavailable, fall back to last auto-saved synthesis. If none exists, start from original input.
Phase 2
Independent Proposals + Research
Agents respond blind -- each critiques before proposing
Each agent responds in isolation -- no agent sees others. Web search enabled. Every agent names one weakness before proposing. Default: 2 Optimizers + Challenger + Simplifier (4 agents, matching DeepM…
Optimizer AOptimizer BChallengerSimplifier
Defends: Sycophancy, Majority tyranny, Bluster effect, No new signal, Complexity creep
2 retries per agent. Continue with N-1 if one fails (min 2). Auto-save each response. Never skip Challenger.
Phase 3
Challenge Register + Arbitration
Challenges logged with steel man -- arbitrated by Coordinator in same call as Phase 04
Challenger objections extracted into numbered register with: steel man, objection, evidence, confidence. Relevant agents defend or concede. Phases 03 and 04 are a SINGLE Coordinator API call. The Coor…
Defends: Decision oscillation, Bluster effect
If defense calls fail, challenge marked 'undefended' but NOT auto-adopted. Coordinator still decides.
Single API call that handles both challenge arbitration and synthesis. Receives: all blind agent proposals, the challenge register with defenses, Simplifier flags, dimension weights, and a ROSTER NOTE…
Most critical call. 3 retries. If fails, save raw responses as fallback. Surface to user.
Phase 5
Independent Verification
Score, diff, sunset, trajectory -- checks synthesis against raw proposals
One API call, structured JSON output. The Verifier sees raw agent proposals alongside the Coordinator's synthesis. If the Coordinator silently dropped an agent's high-confidence point, the raw_vs_synt…
If fails, proceed but flag verification skipped. Double-verify next round.
Phase 6
Compress Context + Minority Report
Decided / active / minority / original -- 800 token hard cap with triage
Compress to four sections with TRIAGE PRIORITY if 800 tokens is tight: (1) ORIGINAL CONSTRAINTS -- verbatim, never cut, always first priority. (2) DECIDED -- locked decisions, bullet points only. (3)…
Defends: Context drift, Consensus inertia
If fails, pass raw synthesis. Always auto-save to storage.
Phase 7
Decision Gate + Human Review
Continue / fork / stop -- with oscillation detection + human override
Five checks: (1) OSCILLATION DETECTION -- hash key decisions and compare to prior rounds. (2) HUMAN REVIEW -- optional pause. (3) CONTINUE -- scores improved, active items remain. (4) FORK -- incompat…
Defends: Sycophancy, Complexity creep
Lightweight check, no API call. Cannot fail.
Phase 8
Output Formatting
Clean deliverable with scoring context and tradeoff transparency
Separate API call. Receives synthesis + verification JSON. The formatter strips debate rhetoric but retains actionable metadata: dimension scores, known tradeoffs, constraint renegotiations, and open…
Output Formatter
2 retries. If fails, present raw synthesis with scores appended. Save regardless.
Prompts
How To Use These Prompts
Read first -- paste order, customization guide, and API budget.
235tok
Failure Modes
Sycophancy
Agents agree more each round. RLHF rewards agreement over accuracy.
Majority Tyranny
If most agents agree (right or wrong), dissenters conform.
Small errors compound 17x through unstructured chains.
Context Drift
Original constraints forgotten. Semantic mutation shifts language.
Consensus Inertia
Wrong decisions solidify. Existing choices stay by default.
No New Signal
Same model, different personas = same knowledge, no diversity.
Role Bleed
Shared training biases regardless of persona.
Complexity Creep
Each round adds without resistance. By round 5, unusable.
Key Principles
Blind Deliberation — Agents never see each other. Eliminates sycophancy, conformity, bluster.
Web Search Per Agent — Different search terms = genuine new information. Primary diversity source.
Mandatory Critique Before Proposal — Every agent names one weakness before suggesting. Distributes challenger energy.
Steel Man Before Objection — Challenger articulates strongest version before attacking. Filters cheap shots.
Confidence-Weighted Decisions — Agents report 1-10 confidence. Coordinator weights evidence over rhetoric, referencing dimension weights (1-3) for tradeoffs. From ReConcile.
Dimension Weighting — Each scoring dimension has a weight (1-3) encoding user priorities. Coordinator references weights when tradeoffs conflict.
Simplifier With Veto — Tests against overwhelmed user on worst day standard. Without active complexity fighting, every round adds bloat.
Simulator Rotation — End-user persona swaps for Optimizer B via useSimulator flag. Drives more structural changes than any expert.
Challenge Register + Combined Arbitration — Logged, arbitrated, decided in a single Coordinator call combined with synthesis. No auto-adoption. Prevents oscillation.
Constraint Renegotiation — If a constraint is infeasible, agents must formally flag it: name it, explain why, propose modified version. Never silently drop.
Research Sources
Talk Isn't Always CheapWynn et al. (2025)Debate decreases accuracy over time. Models favor agreement over challenging flawed reasoning.
7-phase pipeline with blind deliberation, web search, challenge register
vv2.1Session 1base
8 failure modes, 8 principles, 7 research sources
vv3.0Deep auditcritical
Added Simplifier role -- complexity creep was undefended
vv3.0Deep auditcritical
Added Phase 08 Output Formatting -- raw synthesis not usable
vv3.0Deep auditnovel
Mandatory critique-before-proposal for all agents
vv3.0Deep auditnovel
Steel man requirement for Challenger
vv3.3Currentcritical
Phases 03+04 combined into single Coordinator call -- saves 1 API call per round
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Phase 2: Independent Proposals + Research
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Phase 3: Challenge Register + Arbitration
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Phase 4: Coordinator Synthesizes
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Phase 5: Independent Verification
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Phase 6: Compress Context + Minority Report
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Phase 7: Decision Gate + Human Review
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Phase 8: Output Formatting
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HOW TO USE THESE PROMPT TEMPLATES
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PASTE ORDER (new Claude conversation or Project):
1. MASTER PROMPT -- paste first. Replace the three [REPLACE] sections.
2. AGENT DEFINITIONS -- paste with: "Use these agent definitions."
3. SCORING DIMENSIONS -- paste with: "Use these scoring dimensions."
4. ITERATION MODES -- paste with: "Use these iteration modes."
5. STRUCTURAL RULES -- paste with: "Embed this as CTX constant."
CUSTOMIZATION:
- [REPLACE: ...] markers show what to customize.
- The 7 structural ROLES are universal. Keep them for any project.
- The 10 structural RULES are universal. Keep as-is.
- SCORING DIMENSIONS and AGENT EXPERTISE are domain-specific. Replace fully.
API CALL BUDGET (revised v3.3):
- Per round: ~7-9 calls
- Full 3-round debate: ~25-31 total + 1 output formatter
- Estimated time: 3-6 minutes per full debate
- Estimated cost: ~$0.50-2.00 per debate with Sonnet
Master Prompt
Paste first. Instructs Claude to build the debate dashboard.
291tok
Build me a React (.jsx) multi-agent adversarial debate dashboard. Single-file default-export React component.
PROJECT CONTEXT
Domain: [REPLACE: describe your problem domain]
Starting input: [REPLACE: paste your starting material]
User priorities: [REPLACE: list quality priorities and tradeoff preferences]
ARCHITECTURE -- 8-PHASE PIPELINE (v3.3)
Phase 01: Input Preparation
Phase 02: Independent Proposals + Research
Phase 03+04: Challenge Register + Coordinator Synthesis (COMBINED CALL)
Phase 05: Independent Verification
Phase 06: Compress Context + Minority Report
Phase 07: Decision Gate
Phase 08: Output Formatting
STRUCTURAL RULES:
(1) Agents are BLIND
(2) Must name one weakness BEFORE proposing
(3) Challenger must STEEL MAN before objecting
(4) Simplifier has VETO
(5) Challenges REGISTERED and individually arbitrated
(6) Overruled positions PRESERVED in Minority Report
(7) Coordinator evaluates by confidence + evidence, not rhetoric
(8) Word limits enforced per agent
(9) Be specific: exact numbers, measurements, specifications
(10) Max 3 rounds. Auto-stop on score delta < 0.5
(11) CONSTRAINT RENEGOTIATION: formally flag, never silently drop
Decision Oscillation
Round N undoes Round N-1. Binding rules flip decisions.
Coordinator Hedging
Averages positions instead of deciding. Non-committal.
Minority Report
— Overruled dissent preserved with evidence. Auto-resurfaced if scores drop next round.
Context Compression — Decided/Active/Minority/Original (verbatim). Hard cap at 800 tokens. Triage cut order defined.
Score Trajectory — Track trends across rounds, not just current values. Flag declining dimensions and tradeoffs.
Verifier Independence — Verifier sees BOTH the Coordinator's synthesis AND raw agent proposals. Catches points the Coordinator silently dropped.
Oscillation Detection — Hash top 5 key decisions per round. If hash matches a prior round, auto-fork. Prevents cycling between two states.
Max 3 Rounds + Auto-Stop — Sycophancy intensifies after round 2-3. Auto-stop on delta < 0.5 or oscillation.
Decision Gates + Human Override — Continue/Fork/Stop. Some questions need real-world data, not more debate. Human can pause, review, inject constraints at any gate.
When Helpfulness BackfiresNature Digital Medicine (2025)Up to 100% sycophantic compliance. Explicit rejection hints improve critical reasoning.
Towards Understanding SycophancySharma et al. -- ICLR 2024Sycophancy is general RLHF behavior. Matching user views preferred even when incorrect.
Rethinking Multi-Agent WorkflowsXu et al. (2026)Single-agent matches homogeneous multi-agent. Only wins with genuinely different information.
Reliable Decision-MakingLee et al. / Hitachi -- AAAI 2025Multi-agent achieves zero variance. Spoke-and-wheel outperforms decentralized debate.
AI Agent Reflection PatternsZylos Research (2026)Critics should use specific evaluation criteria, not open-ended find problems. Oscillation safeguards: max iteration limits, measurable improvement tracking, state-hash deduplication.
vv3.3Currentcritical
Verifier independence: now receives raw agent proposals alongside synthesis
vv3.3Currentnovel
DIMS scale fields injected into Verifier prompt at build time -- scoring anchored consistently across rounds