Self Evolution Engine

Self-Evolution Engine

Autonomous learning and improvement system that continuously evolves agent behavior based on interaction patterns, feedback, and outcomes.

Core Concepts

Evolution Cycle

Experience → Pattern Detection → Learning → Validation → Integration
     ↑                                                        ↓
     └────────────────── Feedback Loop ←───────────────────────┘

Key Components

| Component | Purpose | Frequency | |-----------|---------|-----------| | Observer | Capture interaction patterns | Continuous | | Analyzer | Identify improvement opportunities | Daily | | Learner | Extract actionable rules | On trigger | | Validator | Test changes in isolation | Before integration | | Integrator | Update behavioral files | After validation |

Quick Start

# Analyze recent interactions
python3 {baseDir}/scripts/evolution.py --analyze --days 7
Extract patterns from memory files
python3 {baseDir}/scripts/evolution.py --extract-patterns
Run self-assessment
python3 {baseDir}/scripts/evolution.py --self-assess
Generate evolution report
python3 {baseDir}/scripts/evolution.py --report --output evolution-report.md

Evolution Data Flow

1. Experience Collection

Sources of experience data:

2. Pattern Detection

Identify recurring patterns:

# Find repeated error patterns
python3 {baseDir}/scripts/evolution.py --pattern errors --threshold 3
Find successful workflows
python3 {baseDir}/scripts/evolution.py --pattern successes --min-occurrences 5
Find optimization opportunities
python3 {baseDir}/scripts/evolution.py --pattern inefficiencies

Pattern categories:

3. Learning Extraction

Transform patterns into actionable rules:

# Auto-extract learnings
python3 {baseDir}/scripts/evolution.py --learn --auto
Interactive learning session
python3 {baseDir}/scripts/evolution.py --learn --interactive

Output: Candidate rules for behavioral files

4. Validation

Test proposed changes:

# Validate a proposed change
python3 {baseDir}/scripts/evolution.py --validate --rule "Always use git status before commit"
Simulate behavior change
python3 {baseDir}/scripts/evolution.py --simulate --file SOUL.md --change "Be more concise"

5. Integration

Apply validated changes:

# Apply to behavioral files
python3 {baseDir}/scripts/evolution.py --integrate --target SOUL.md
Update workflow rules
python3 {baseDir}/scripts/evolution.py --integrate --target AGENTS.md

Behavioral Evolution Targets

SOUL.md (Personality & Principles)

Evolution triggers:

Example evolutions:

# Before
"Be helpful and thorough"
After (evolved)
"Be concise and direct. Skip disclaimers. Act, don't explain."

AGENTS.md (Workflows & Rules)

Evolution triggers:

Example evolutions:

# Before
"Check files before editing"
After (evolved)
"Always read file first. Use edit tool only after confirming structure.
For files >500 lines, read in chunks with offset/limit."

TOOLS.md (Tool Knowledge)

Evolution triggers:

Example evolutions:

# Added after learning
agent-browser
Always use --json for parsing
Wait 2s after navigation before snapshot
Close browser after each session to prevent memory leak

Pattern Recognition

Error Pattern Detection

# Find recurring errors
python3 {baseDir}/scripts/evolution.py \
  --analyze errors \
  --source .learnings/ERRORS.md \
  --threshold 3 \
  --output patterns/errors.json

Example pattern:

{
  "pattern_id": "ERR-PATTERN-001",
  "description": "File not found errors when using relative paths",
  "occurrences": 5,
  "first_seen": "2025-01-10",
  "last_seen": "2025-01-20",
  "suggested_rule": "Always resolve paths relative to workspace root",
  "target_file": "AGENTS.md"
}

Success Pattern Detection

# Find successful workflows
python3 {baseDir}/scripts/evolution.py \
  --analyze successes \
  --source memory/ \
  --min-effectiveness 0.8

User Preference Learning

# Extract user preferences from corrections
python3 {baseDir}/scripts/evolution.py \
  --analyze preferences \
  --source .learnings/LEARNINGS.md \
  --category correction

Evolution Metrics

Track evolution effectiveness:

# Generate metrics
python3 {baseDir}/scripts/evolution.py --metrics --period 30d
Output

| Metric | Description | Target | |--------|-------------|--------| | Error Reduction Rate | % decrease in recurring errors | >50% | | Rule Adoption Rate | % of proposed rules integrated | >70% | | User Satisfaction Trend | Positive feedback ratio | >0.8 | | Efficiency Gain | Time saved per interaction | Measurable | | Learning Velocity | New rules per week | Sustainable |

Automated Evolution

Periodic Self-Assessment

Add to heartbeat or cron:

# Weekly self-assessment
python3 {baseDir}/scripts/evolution.py --self-assess --auto-evolve
Output to evolution log
python3 {baseDir}/scripts/evolution.py --self-assess --log evolution-log.md

Integration with Self-Improvement Skill

This skill builds on self-improvement:

1. self-improvement logs individual learnings 2. self-evolution analyzes patterns across learnings 3. self-evolution proposes behavioral changes 4. self-improvement tracks the change as a learning

Workflow:

# Log a learning (self-improvement)
→ .learnings/LEARNINGS.md
Pattern detection (self-evolution)
python3 {baseDir}/scripts/evolution.py --analyze --source .learnings/
Proposed change appears
→ "Pattern: 5 occurrences of 'forgot to read file first'"
Validate and integrate
python3 {baseDir}/scripts/evolution.py --integrate --approve
→ AGENTS.md updated
Track as learning (self-improvement)
→ "Promoted rule: Always read before edit"

Evolution Rules

When to Evolve

Trigger evolution when:

| Signal | Threshold | Action | |--------|-----------|--------| | Same error 3+ times | Pattern detected | Create prevention rule | | User correction pattern | 2+ similar corrections | Update behavior | | Workflow optimization | 20%+ efficiency gain | Update workflow | | Tool discovery | New capability found | Update TOOLS.md | | Preference pattern | Consistent user preference | Update SOUL.md |

What to Evolve

| File | Evolution Type | Frequency | |------|----------------|-----------| | SOUL.md | Personality, principles | Rarely | | AGENTS.md | Workflows, rules | Often | | TOOLS.md | Tool knowledge | As discovered | | MEMORY.md | Key facts | Continuously |

Evolution Safeguards

Before any evolution:

1. Validate - Test in isolation 2. Review - Check for conflicts 3. Backup - Save current state 4. Reversible - Ensure can rollback 5. Log - Track all changes

# Create backup before evolution
python3 {baseDir}/scripts/evolution.py --backup
Rollback if needed
python3 {baseDir}/scripts/evolution.py --rollback --to "2025-01-20"

Reports

Evolution Report

# Generate comprehensive report
python3 {baseDir}/scripts/evolution.py --report --full
Output

# Evolution Report: 2025-01-20
Patterns Detected
3 error patterns (2 addressed)
5 success patterns (3 documented)
2 preference patterns (integrated)
Rules Proposed
1. "Always read file before editing" → AGENTS.md
2. "Prefer concise over thorough" → SOUL.md
Metrics
Error reduction: 45%
User satisfaction: 0.85
Efficiency gain: 12%
Next Actions
Validate rule #1
Review preference pattern #2

Diff Report

# Show what changed over time
python3 {baseDir}/scripts/evolution.py --diff --since "30 days ago"

Advanced Usage

Custom Pattern Detectors

Create custom detectors in scripts/detectors/:

# scripts/detectors/custom_detector.py
def detect_pattern(entries):
    """Custom pattern detection logic"""
    # Return list of detected patterns
    pass

Register:

python3 {baseDir}/scripts/evolution.py \
  --register-detector custom_detector \
  --path scripts/detectors/custom_detector.py

Evolution Hooks

Trigger evolution on specific events:

# hooks/evolution-hooks.yaml
on_error:
  - pattern: "file not found"
    action: "analyze"
    threshold: 3
on_user_correction:
  - action: "learn_preference"
    immediate: true
on_workflow_success:
  - action: "document_pattern"
    min_repetitions: 3

Integration Points

With longterm-memory skill

# Use memory context for evolution
python3 {baseDir}/scripts/evolution.py --analyze --with-memory
Propose rules based on memory patterns
python3 {baseDir}/scripts/evolution.py --extract-patterns --source MEMORY.md

With self-improvement skill

# Feed patterns to self-improvement
python3 {baseDir}/scripts/evolution.py --feed-to self-improvement
Use learnings as evolution source
python3 {baseDir}/scripts/evolution.py --analyze --source .learnings/

Best Practices

1. Run analysis regularly - Weekly or bi-weekly 2. Validate before integrating - Never auto-integrate without validation 3. Keep evolution log - Track all changes and reasons 4. Measure impact - Track metrics before/after changes 5. Human oversight - Significant changes should be reviewed 6. Rollback ready - Always maintain ability to revert 7. Conservative approach - Better to miss an optimization than break behavior

Notes

# Analyze recent interactions
python3 {baseDir}/scripts/evolution.py --analyze --days 7
Extract patterns from memory files
python3 {baseDir}/scripts/evolution.py --extract-patterns
Run self-assessment
python3 {baseDir}/scripts/evolution.py --self-assess
Generate evolution report
python3 {baseDir}/scripts/evolution.py --report --output evolution-report.md

1. Run analysis regularly - Weekly or bi-weekly 2. Validate before integrating - Never auto-integrate without validation 3. Keep evolution log - Track all changes and reasons 4. Measure impact - Track metrics before/after changes 5. Human oversight - Significant changes should be reviewed 6. Rollback ready - Always maintain ability to revert 7. Conservative approach - Better to miss an optimization than break behavior