Self-Evolution

Commander improves over time through a meta-learning system that tunes agent configurations based on execution outcomes. The system combines Thompson Sampling and Reflexion to optimize topology selection, provider choice, and parameter settings.

Architecture

Execution completes
  │
  ├─ TrajectoryAnalyzer       ← Analyze execution patterns
  │   └─ Extract features: duration, tokens, success rate
  │
  ├─ MetaLearner              ← Thompson Sampling
  │   └─ Update Beta distributions per arm
  │
  └─ EvolverAgent             ← Cross-run optimization
      └─ Propose configuration changes

MetaLearner

Uses Thompson Sampling with Beta distributions to balance exploration and exploitation:

interface Arm {
  name: string;             // e.g., "topology:DISPATCH"
  alpha: number;            // Success count
  beta: number;             // Failure count
}

class MetaLearner {
  selectArm(arms: Arm[]): Arm {
    // Sample from each arm's Beta distribution
    // Pick the arm with the highest sampled value
    return arms.reduce((best, arm) => {
      const sample = BetaDistribution.sample(arm.alpha, arm.beta);
      return sample > best.sample ? { ...arm, sample } : best;
    });
  }

  updateArm(arm: Arm, success: boolean): void {
    if (success) arm.alpha++;
    else arm.beta++;
  }
}

Each configuration variant (topology, provider, retry strategy) is modeled as an arm. Over thousands of executions, the system converges on the optimal configuration for each task type.

TrajectoryAnalyzer

Analyzes execution traces to extract patterns that inform the meta-learner:

• Step duration distribution — Which steps are bottlenecks
• Token consumption patterns — Context window utilization
• Retry and failure patterns — Which gates fail most often
• Provider performance — Latency, error rates per provider

const analyzer = new TrajectoryAnalyzer();
const patterns = analyzer.analyze(trajectory);

// patterns.bottlenecks     → [step names sorted by duration]
// patterns.commonFailures  → [failure modes by frequency]
// patterns.providerRanking → [providers sorted by success rate]

EvolverAgent

The EvolverAgent bridges analysis to action. It generates configuration recommendations:

• If DISPATCH topology fails 30% more often than CHAIN for RESEARCH tasks, it adjusts the topology router's default mapping
• If a provider consistently times out, it moves that provider later in the fallback chain
• If verification consistently catches hallucination in code tasks, it tightens the hallucination threshold for code outputs

Reflexion

Commander implements Reflexion — post-execution self-evaluation — as a feedback mechanism:

1. After each run, the system evaluates its own performance
2. Success/failure signals update the meta-learner's Beta distributions
3. Long-term patterns trigger configuration adjustments via the evolver agent

Outcomes

The self-evolution system produces measurable improvements:

Metric	Impact
Topology selection accuracy	+15-20% after 1000 runs
Provider availability	Failover optimized for current latency
Verification threshold tuning	False positives reduced by 30%
Token efficiency	Budget allocation converges to task need