Agent Meta Process Mining — autonomous agent review and optimization recommendations

[vybe]

Summary

A meta-agent that autonomously reviews the activity, logs, and behavior of other Trinity agents to extract process-level and operational recommendations for improvement.

This agent acts as an internal auditor/consultant — it doesn't do the work itself, but observes how other agents work and identifies what can be improved.

Problem

As the number of deployed agents grows, there is no systematic way to:

• Understand how agents actually operate vs. how they were designed to operate
• Identify inefficiencies, repeated failures, or suboptimal patterns
• Surface recommendations for prompt tuning, tool usage, or workflow restructuring
• Detect drift in agent behavior over time

Operators currently have to manually review logs and guess at improvements.

Proposed Solution

Core Capabilities

1. Activity Review & Log Mining

   • Ingest agent execution logs (via Vector/JSON log files)
   • Parse task sequences, tool call patterns, error rates, retry loops
   • Build process models from observed behavior (process discovery)

2. Process Analysis

   • Identify bottlenecks (steps that consistently take longest or fail most)
   • Detect repeated patterns that could be consolidated or templated
   • Find unused or underutilized tools/capabilities
   • Spot circular reasoning or retry storms
   • Compare actual execution paths against intended workflows (conformance checking)

3. Operational Analysis

   • Resource utilization (token spend per task, cost efficiency)
   • Success/failure rates by task type
   • Time-to-completion trends
   • Error categorization and root cause clustering
   • Credential usage patterns (which tools/APIs are actually used)

4. Recommendation Engine

   • Generate actionable recommendations per agent:
     • Prompt improvements (e.g., "Agent X frequently misinterprets step 3 — consider adding examples")
     • Tool configuration (e.g., "Agent Y never uses tool Z despite having it configured")
     • Workflow restructuring (e.g., "Steps 4-6 could be parallelized")
     • Resource optimization (e.g., "Agent W uses 3x more tokens than similar agents for the same task type")
   • Prioritize recommendations by estimated impact
   • Track whether recommendations were adopted and measure improvement

5. Reporting

   • Per-agent health/efficiency scorecards
   • Fleet-wide trends and anomalies
   • Periodic summary reports (daily/weekly)
   • Dashboard integration with key metrics

Architecture Considerations

• Runs as a Trinity agent itself (dogfooding the platform)
• Read-only access to other agents' logs and metadata (no direct control)
• Could use the existing Vector log pipeline as its primary data source
• Results stored as structured reports accessible via API and dashboard
• Should support both on-demand analysis and scheduled periodic reviews

Example Output

Agent: scout
Period: Last 7 days
Tasks Analyzed: 47

PROCESS FINDINGS:
- 🔄 Retry storm detected: 12/47 tasks had >3 retries on web search step
- 📊 Sequential bottleneck: Research → Summarize → Format runs serially but
  Summarize and Format could overlap
- ⚠️ Tool drift: MCP tool "fetch_url" called 0 times despite being configured

OPERATIONAL FINDINGS:  
- 💰 Token efficiency: 34% above fleet average for comparable research tasks
- ❌ Failure rate: 8.5% (fleet average: 3.2%), primarily timeout-related
- ⏱️ Avg completion: trending upward over past 3 weeks

RECOMMENDATIONS:
1. [HIGH] Add retry backoff to web search tool configuration
2. [MEDIUM] Restructure research prompt to enable parallel summarization
3. [LOW] Remove unused fetch_url tool to reduce prompt size
4. [HIGH] Investigate timeout root cause — likely upstream API latency

Relationship to Existing Features

• Builds on Vector log aggregation (existing infrastructure)
• Complements Operating Room (adds analytical layer on top of monitoring)
• Could feed into agent template improvements (data-driven template evolution)
• Related to process-miner skill (but operates at fleet level, not single-session)

Open Questions

• Should recommendations be auto-applied (with approval) or purely advisory?
• What's the right granularity — per-task, per-agent, or per-fleet?
• Should it have the ability to propose and create modified agent templates?
• Integration with GitHub Issues for tracking recommendation adoption?

Acceptance Criteria

• Meta agent can ingest logs from any running Trinity agent
• Process discovery produces accurate workflow models from log data
• Generates per-agent scorecards with actionable recommendations
• Recommendations are prioritized by estimated impact
• Dashboard view for fleet-wide process health
• Scheduled periodic analysis (configurable frequency)
• Tracks recommendation adoption and measures improvement over time