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Last-resort multi-model panel via OpenRouter Fusion. Only invoked when all other approaches (local model, single frontier, existing skills) have failed on the same problem. Architecture: - Problem classification (lean_proof, code_bug, architecture, math_novel, infra_debug) - Per-type model panel selection (Opus + DeepSeek + Gemini for proofs) - Context assembly (files, build logs, AGENTS.md constraints) - OpenRouter Fusion call (parallel panel + judge synthesis) - Structured output (diagnosis, solution, alternatives, risks, cost) Safety: - Explicit 'break glass:' trigger required - Cost gate: /usr/bin/bash.30-.50 per invocation, .00/session cap - Rate limit: 3 invocations per session - Audit trail via ContextStream Files: - 4-Infrastructure/shim/break_glass_mcp.py (MCP server) - 6-Documentation/docs/specs/break_glass_mcp_agent.md (design spec) - .opencode/skills/break-glass/SKILL.md (skill definition) - .mcp.json (server registration)
301 lines
11 KiB
Markdown
301 lines
11 KiB
Markdown
# break-glass MCP Agent — Design Spec
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**Purpose:** A last-resort agent that assembles a focused multi-model panel to
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attack novel issues that have resisted all other approaches.
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**Cost model:** Each invocation costs 3-5× a single model call. The gate
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ensures it's only triggered when the alternative is human time (more expensive).
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---
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## Trigger Protocol
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The agent MUST NOT be called automatically. It requires an explicit trigger:
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```
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break glass: <problem statement>
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```
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Before invoking, the caller MUST have already tried:
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1. The local model (Hermes3 / current default)
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2. A single frontier model (Claude Opus / GPT-5.5)
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3. The existing skill/tool system
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Only when all three fail on the SAME problem should the trigger fire.
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---
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## Architecture
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```
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┌─────────────────┐
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│ Caller (agent │
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│ or human) │
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│ │
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│ "break glass: │
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│ cleanMerge_ │
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│ preservesGap │
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│ sorry" │
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└────────┬────────┘
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│
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▼
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┌─────────────────┐ ┌──────────────────────┐
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│ DIAGNOSE │ │ Context Assembly │
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│ │────▶│ │
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│ - Parse problem│ │ - Read relevant files│
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│ - Classify type│ │ - Extract sorry/ │
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│ - Estimate │ │ error context │
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│ complexity │ │ - Pull AGENTS.md │
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│ │ │ constraints │
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└────────┬────────┘ │ - Gather build logs │
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│ └──────────┬───────────┘
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│ │
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▼ ▼
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┌─────────────────────────────────────────────┐
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│ PANEL SELECTION │
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│ │
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│ Problem type → Model panel: │
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│ │
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│ lean_proof → [Claude Opus, DeepSeek V4, │
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│ Gemini 3.1 Pro] │
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│ code_bug → [GPT-5.5, Claude Fable, │
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│ Kimi K2.6] │
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│ architecture → [Claude Opus, GPT-5.5, │
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│ Gemini 3.1 Pro] │
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│ math_novel → [DeepSeek V4 Pro, Claude │
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│ Opus, Gemini 3.1 Pro] │
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│ infra_debug → [GPT-5.5, Claude Opus, │
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│ DeepSeek V4 Pro] │
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└────────┬────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────┐
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│ OPENROUTER FUSION CALL │
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│ │
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│ POST https://openrouter.ai/api/v1/ │
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│ chat/completions │
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│ │
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│ { │
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│ "model": "openrouter/fusion", │
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│ "plugins": [{ │
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│ "id": "fusion", │
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│ "model": "anthropic/claude-opus-4.8", │
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│ "analysis_models": [panel...] │
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│ }], │
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│ "messages": [ │
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│ {"role": "system", "content": "..."}, │
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│ {"role": "user", "content": "..."} │
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│ ] │
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│ } │
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└────────┬────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────┐
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│ JUDGE / SYNTHESIZE │
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│ │
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│ The Fusion judge model: │
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│ - Identifies consensus across panel │
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│ - Flags contradictions │
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│ - Extracts unique insights │
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│ - Identifies blind spots │
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│ - Produces structured analysis │
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└────────┬────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────┐
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│ OUTPUT │
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│ │
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│ Structured response: │
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│ - Problem diagnosis │
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│ - Proposed solution (with confidence) │
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│ - Alternative approaches │
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│ - Risks and caveats │
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│ - Cost report │
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└─────────────────────────────────────────────┘
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```
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---
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## System Prompt Template
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```
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You are a specialist agent assembled to attack a specific novel problem
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that has resisted常规 approaches. You have access to:
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1. The full codebase context (provided below)
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2. The project's operating constraints (AGENTS.md)
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3. The specific problem statement
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4. Previous attempts and their failure modes
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YOUR TASK:
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- Diagnose the root cause
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- Propose a concrete solution
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- If the solution requires changes, specify exact file paths and code
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- If the problem is undecidable with current tools, explain why and
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propose what additional information or tools would be needed
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CONSTRAINTS:
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- Lean is the source of truth for formal claims
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- No Float in compute paths (use Q0_16 or Q16_16)
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- No sorry in committed code (unless with TODO(lean-port) + human sign-off)
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- All new formal work goes to SilverSight (not Research Stack)
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OUTPUT FORMAT:
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{
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"diagnosis": "...",
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"solution": {
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"approach": "...",
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"confidence": 0.0-1.0,
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"files_to_change": [...],
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"code_snippets": {...}
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},
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"alternatives": [...],
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"risks": [...],
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"cost_estimate": "$X.XX"
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}
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```
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---
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## Context Assembly
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Before calling Fusion, the agent assembles context:
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```python
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def assemble_context(problem: str) -> dict:
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# 1. Parse problem type
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problem_type = classify_problem(problem)
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# lean_proof | code_bug | architecture | math_novel | infra_debug
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# 2. Read relevant files
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files = find_relevant_files(problem) # grep/glob based
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# 3. Extract build logs
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build_log = run_lake_build_if_lean(problem)
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# 4. Pull constraints
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constraints = read_agents_md()
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# 5. Check previous attempts
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history = search_contextstream(problem)
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return {
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"problem": problem,
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"type": problem_type,
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"files": files,
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"build_log": build_log,
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"constraints": constraints,
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"history": history,
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}
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```
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---
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## Cost Gate
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```python
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COST_THRESHOLD = {
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"lean_proof": 0.50, # $0.50 per invocation
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"code_bug": 0.30, # $0.30
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"architecture": 1.00, # $1.00 (most expensive, most valuable)
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"math_novel": 0.75, # $0.75
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"infra_debug": 0.40, # $0.40
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}
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def should_proceed(problem_type: str, estimated_cost: float) -> bool:
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threshold = COST_THRESHOLD.get(problem_type, 0.50)
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if estimated_cost > threshold * 3:
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return False # Too expensive, escalate to human
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return True
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```
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---
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## MCP Server Definition
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```json
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{
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"name": "break-glass",
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"description": "Last-resort multi-model panel for novel problems",
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"tools": [
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{
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"name": "break_glass",
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"description": "Assemble a focused multi-model panel to attack a problem that has resisted all other approaches. Costs 3-5× a single model call. Requires explicit 'break glass:' trigger.",
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"parameters": {
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"type": "object",
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"properties": {
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"problem": {
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"type": "string",
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"description": "The specific problem statement"
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},
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"context_files": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Specific files to include in context"
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},
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"problem_type": {
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"type": "string",
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"enum": ["lean_proof", "code_bug", "architecture", "math_novel", "infra_debug"],
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"description": "Problem classification (auto-detected if omitted)"
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},
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"budget": {
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"type": "number",
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"description": "Maximum cost in USD (default: 1.00)"
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}
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},
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"required": ["problem"]
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}
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}
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]
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}
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```
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---
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## Example Invocation
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**Human:** "break glass: cleanMerge_preservesGap sorry — I've tried native_decide,
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canonicalize+simp, and exhaustive list match. The List.zip/filter/all terms are
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too large for simp to reduce."
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**Agent response:**
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```
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DIAGNOSIS: The bridge lemma requires showing that verifySpectralGap on an
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8-element Q16_16 list equals boolGapPat on the boolean pattern. The simp
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tactic can't reduce List.zip (List.range 8) [a0,...,a7] |>.filter |>.map
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|.all to the equivalent Bool expression because intermediate terms exceed
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simp's working memory.
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SOLUTION: Define a @[reducible] helper that mirrors verifySpectralGap but
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on Fin 8 → Bool, prove it equals boolGapPat by native_decide, then show
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verifySpectralGap = helper ∘ (· != 0) by pattern-matching on 8-element
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lists and using canonicalize_ne_zero.
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[exact code provided]
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CONFIDENCE: 0.85
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ALTERNATIVES:
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1. Custom simp lemma for 8-element List.zip (lower confidence)
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2. Rewrite verifySpectralGap to use Fin 8 directly (invasive)
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RISKS: The @[reducible] approach may cause simp slowdowns elsewhere
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COST: $0.47
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```
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---
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## Integration Points
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1. **MCP server** — `break-glass` tool in `.mcp.json`
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2. **Skill** — `break-glass` skill that loads the system prompt
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3. **ContextStream** — logs every invocation for cost tracking
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4. **AGENTS.md** — documents the trigger protocol
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5. **Cost tracking** — ContextStream memory events for each invocation
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---
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## Safety
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- **Rate limit:** Max 3 invocations per session
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- **Cost cap:** $5.00 per session (configurable)
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- **Human gate:** If estimated cost > $2.00, require human confirmation
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- **Audit trail:** Every invocation logged to ContextStream with cost, problem, and outcome
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