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Magnus Hedemark
3a4aad40bf
Spawns a panel of expert agents to debate any question with convergence-aware iteration and typed synthesis output. - PydanticAI + PydanticGraph-based Python package - 5-phase debate protocol: compose → premortem → position → cross-examine (iterative, eval-driven) → synthesis - Convergence detection: confidence dispersion, argument novelty, concession rate — stops when diminishing returns - Typed output schemas (Pydantic models) for every phase - CLI tool with markdown and JSON output modes - Custom persona file support - Bootstrap detection: sys.executable -m pip install fallback - agentskills.io compatible SKILL.md with triggers Signed-off-by: Magnus Hedemark <magnus919@pm.me>
68 lines
2.7 KiB
Markdown
68 lines
2.7 KiB
Markdown
# Debate Protocol
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## Phase Structure
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### Phase 1: Compose
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A single LLM call generates `N` expert personas. The prompt prioritizes **diversity of initial position** over diversity of expertise. Each persona includes:
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- Name
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- Career background (one paragraph)
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- Specific expertise
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- Analytical approach
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- Bias or experience they bring to the specific question
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At least one agent is structurally skeptical (light red-team). At least one approaches from a fundamentally different cognitive frame.
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### Phase 2: Premortem
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Each agent independently writes how the decision **already failed** — before any positions are formed. This bypasses positional commitment bias. Agents do NOT see each other's premortems.
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The premortem output includes:
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- Failure scenario (narrative)
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- Root causes
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- Early warning signals
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### Phase 3: Position
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Each agent forms an independent position. They see their own premortem (for continuity) but NOT other agents' positions or premortems.
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Position output includes:
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- Stance
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- Reasoning chain
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- Confidence score (0-1)
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- Key assumptions
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### Phase 4: Cross-Examination (Iterative)
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Each agent reads all other agents' positions and responds. They see:
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- Every other agent's stance, reasoning, confidence, and assumptions
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- Their own previous round's reflection, concessions, and remaining disagreements
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Cross-examination output includes:
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- Concessions (where the other agent's reasoning was stronger)
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- Remaining disagreements (what's still in dispute)
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- Updated position (if changed)
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- Updated confidence (if changed)
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- Reflection on what they learned
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- New evidence needed to close gaps
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After each round, convergence detection runs:
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- If converged → proceed to synthesis
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- If diminishing returns → proceed to synthesis
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- If genuine disagreement → proceed to synthesis (with divergence report)
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- If more debate needed → run another round (up to max_rounds)
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### Phase 5: Synthesis
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The synthesis combines algorithmic metrics (confidence dispersion, argument novelty) with an LLM-generated narrative. The output preserves the distinction between:
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- **Pre-positional risks** (from premortem — uncontaminated by positional commitment)
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- **Post-positional concerns** (survived cross-examination — tested against alternatives)
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## Design Principles
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1. **Independent thought first** — agents form positions before seeing others'
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2. **Diversity over expertise** — different approaches beat more expertise with shared framing
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3. **Pre-mortem before position** — surface failure modes before committing to a stance
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4. **Convergence is measured, not assumed** — algorithmic stopping conditions prevent premature or interminable debate
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5. **Tension is the output** — the synthesis surfaces genuine disagreement, not forced consensus
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