OpenClaw Bot
95060930b1
- Add mc_api_call_machine() function for MC_MACHINE_TOKEN auth - Update mc_api_call() to use machine token when available - Allows cron jobs to authenticate without cookie-based login - No breaking changes - cookie auth still works for interactive use - Also updates default API URL to production (was localhost)
165 lines
4.6 KiB
Markdown
165 lines
4.6 KiB
Markdown
# Implementation Plan: Nine Meta-Learning Loops Integration
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## Overview
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Integrate Vox's 9 meta-learning loops framework into Mission Control's autonomous agent system to enable closed-loop operations and progression toward full agent autonomy.
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## Current State Analysis
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**Strengths:**
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- Alice/Bob/Charlie workflow established
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- API-centric CLI pattern prevents duplication
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- Gantt Board provides task orchestration
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- Research → Document → Task pipeline works
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**Gaps:**
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- No cap gates for agent overreach prevention
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- No reaction matrix for standardized responses
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- No proposal service for agent coordination
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- No self-healing/stale task detection
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- Missing autonomy progression tracking
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## Proposed Implementation
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### Phase 1: Safety Mechanisms (Week 1-2)
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**1.1 Cap Gates System**
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```
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Location: /lib/agents/cap-gates.ts
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- Max review cycles: 3 before human escalation
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- Max token spend per task: 100k tokens
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- Max execution time: 2 hours per agent session
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- Forbidden operations: Require explicit approval
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```
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**1.2 Reaction Matrix**
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```
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Location: /lib/agents/reaction-matrix.ts
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Standardized responses for:
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- API failures → Retry with backoff
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- Syntax errors → Check SKILL.md first
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- Test failures → Run debug skill
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- Research complete → Handoff to Bob
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- Implementation stuck → Escalate to human
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```
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### Phase 2: Coordination Layer (Week 3-4)
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**2.1 Proposal Service**
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```
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Location: /lib/agents/proposals/
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- Agent submits proposal: "I want to do X"
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- Check against cap gates
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- Validation against current sprint
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- Auto-approve if within bounds
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- Human approval if exceeds limits
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```
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**2.2 Proposal Protocol**
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```json
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{
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"proposalId": "uuid",
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"agentId": "alice-researcher",
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"type": "research|implement|test",
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"estimatedCost": "tokens",
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"estimatedTime": "minutes",
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"requiresApproval": true|false,
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"rationale": "string",
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"expectedOutput": "string"
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}
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```
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### Phase 3: Self-Healing (Week 5-6)
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**3.1 Stale Task Detection**
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```
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Location: /lib/agents/health-check.ts
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- Cron every 30 minutes
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- Check tasks with status "in-progress" > 30 min
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- Query agent status via sessions_list
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- If agent stalled: Respawn or escalate
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- Update task with diagnostic comment
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```
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**3.2 Recovery Actions**
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```
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- Agent crashed → Respawn with context
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- Agent stuck → Spawn debugger agent
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- Task unclear → Add clarification request
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- Resource exhausted → Queue for off-peak
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```
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### Phase 4: Observability (Week 7-8)
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**4.1 Agent Dashboard (Mission Control Phase 8)**
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- Real-time agent status
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- Token usage per agent
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- Success/failure rates
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- Time-to-completion metrics
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- Autonomy level progression
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**4.2 Learning Metrics**
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- Which patterns succeed most
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- Common failure modes
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- Optimal task sizes
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- Best agent combinations
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## Integration Points
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### With Existing Systems
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| System | Integration Point | Change Required |
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|--------|-------------------|-----------------|
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| Gantt Board | Task status API | Add stale detection trigger |
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| Mission Control | Documents API | Link research → plans |
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| Agent Workflow | Spawn protocol | Add cap gate checks |
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| Session Logs | Query API | Health check queries |
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### File Changes
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```
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NEW: /lib/agents/cap-gates.ts
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NEW: /lib/agents/reaction-matrix.ts
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NEW: /lib/agents/proposal-service.ts
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NEW: /lib/agents/health-check.ts
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NEW: /lib/agents/dashboard.ts
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MODIFY: /agents/TEAM-REGISTRY.md
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MODIFY: Skill files for Alice/Bob/Charlie (add cap checks)
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```
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## Risks and Mitigation
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| Risk | Impact | Mitigation |
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|------|--------|------------|
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| Cap gates too restrictive | Agents can't work | Start permissive, tighten based on data |
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| Proposal overhead | Slower execution | Auto-approve 90% of cases |
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| False stale detection | Interrupted work | Require 3 checks before action |
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| Dashboard complexity | Delayed Phase 8 | Build incrementally |
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## Success Criteria
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- [ ] Zero runaway agent incidents
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- [ ] 95% auto-approval rate for proposals
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- [ ] <5 min stale detection latency
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- [ ] 50% reduction in human intervention needs
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- [ ] Complete audit trail of agent decisions
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## Timeline
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- **Week 1-2:** Cap gates + reactions
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- **Week 3-4:** Proposal service
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- **Week 5-6:** Self-healing
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- **Week 7-8:** Dashboard
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## Dependencies
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- Requires current agent workflow to be stable
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- Gantt Board API token access
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- Session log query capability
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- Session list/monitoring tools
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## Rollout Strategy
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1. Deploy cap gates (observation mode)
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2. Enable reaction matrix
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3. Launch proposal service (with manual approval)
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4. Enable auto-approval after 1 week
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5. Add stale detection
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6. Build dashboard incrementally
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## Verdict: ADOPT
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This plan directly addresses Mission Control's Phase 6-9 roadmap using a proven pattern from Vox. Start with Phase 1 safety mechanisms before enabling more autonomy.
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