149 lines
4.7 KiB
Markdown
149 lines
4.7 KiB
Markdown
# Warm Session Provisioning: Pre-Proficient Agent Sessions
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**Research Document**
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**Issue:** #327
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**Date:** April 2026
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**Status:** Research & Prototype
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## Executive Summary
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Empirical analysis reveals a counterintuitive finding: marathon sessions (100+ messages) exhibit **lower** per-tool error rates (5.7%) than mid-length sessions (9.0% at 51-100 messages). This suggests agents improve with experience within a session, learning user patterns and establishing successful tool-call conventions.
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This research explores whether we can pre-seed sessions with proficiency patterns, effectively creating "warm" sessions that start at marathon-level reliability.
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## Key Findings from Empirical Audit
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### 1. Session Length vs. Error Rate
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- **0-50 messages:** 7.2% error rate
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- **51-100 messages:** 9.0% error rate (peak)
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- **100+ messages:** 5.7% error rate (lowest)
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### 2. Hypothesis: Context Richness Drives Proficiency
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Marathon sessions develop:
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- **User-specific patterns:** How the user phrases requests
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- **Tool-call conventions:** Successful argument formats
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- **Error recovery patterns:** How to handle failures
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- **Context anchoring:** Shared reference points
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### 3. Research Questions
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1. What specific context elements drive proficiency?
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2. Can we extract and transfer these patterns?
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3. Does compression preserve proficiency or reset it?
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4. What's the minimum viable warm-up sequence?
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## Technical Approach
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### Phase 1: Analysis (Current)
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Analyze existing marathon sessions to identify proficiency markers:
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- Tool-call success patterns
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- User interaction conventions
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- Error recovery sequences
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- Context window utilization
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### Phase 2: Template Extraction
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Extract successful patterns from marathon sessions:
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```python
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# Conceptual template structure
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session_template = {
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"tool_patterns": [
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{"tool": "terminal", "success_pattern": "..."},
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{"tool": "file_operations", "conventions": "..."}
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],
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"user_patterns": {
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"request_style": "direct",
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"feedback_style": "terse"
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},
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"recovery_patterns": [
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{"error": "FileNotFound", "recovery": "..."}
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]
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}
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```
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### Phase 3: Warm Session Creation
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Implement session seeding mechanism:
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1. Run 10-20 diverse tasks to build context
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2. Compress the session (preserving proficiency markers)
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3. Save as warm template
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4. New sessions start from template
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### Phase 4: A/B Testing
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Compare warm vs. cold sessions:
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- Same tasks, different starting conditions
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- Measure: error rate, time to completion, user satisfaction
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- Statistical significance testing
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## Implementation Plan
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### 1. Session Profiling System
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- Add telemetry to track proficiency markers
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- Identify what makes marathon sessions successful
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- Extract transferable patterns
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### 2. Template Management
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- Save/load session templates
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- Version control for templates
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- Template validation
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### 3. Warm Session Bootstrapping
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- Inject template context at session start
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- Preserve tool-call conventions
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- Maintain user pattern awareness
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### 4. Research Infrastructure
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- A/B testing framework
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- Statistical analysis tools
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- Visualization of results
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## Expected Outcomes
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### Short-term (Weeks 1-2)
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- Session profiling system operational
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- Initial pattern extraction from 5-10 marathon sessions
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- Basic template storage
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### Medium-term (Weeks 3-4)
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- Warm session prototype
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- Initial A/B test results
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- Paper outline
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### Long-term (Month 2+)
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- Production-ready warm session provisioning
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- Published research paper
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- Open-source template sharing
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## Paper-Worthy Contributions
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1. **Empirical finding:** Session proficiency increases with length
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2. **Novel approach:** Pre-seeding sessions with proficiency patterns
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3. **Production system:** Warm session provisioning infrastructure
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4. **Open dataset:** Session proficiency markers and templates
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## Risks and Mitigations
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### Technical Risks
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- **Compression resets proficiency:** Test different compression strategies
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- **Patterns don't transfer:** Start with similar task domains
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- **Template bloat:** Keep templates minimal, focus on high-impact patterns
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### Research Risks
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- **Statistical insignificance:** Run sufficient A/B test iterations
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- **Confounding variables:** Control for task difficulty, user expertise
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- **Publication bias:** Report all results, including negative findings
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## Next Steps
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1. ✅ Create research framework (this document)
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2. ⏳ Implement session profiling telemetry
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3. ⏳ Analyze 5-10 marathon sessions
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4. ⏳ Extract proficiency patterns
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5. ⏳ Build template storage system
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6. ⏳ Implement warm session bootstrapping
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7. ⏳ Run A/B tests
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8. ⏳ Write paper
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## References
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- Empirical Audit 2026-04-12, Finding 4
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- Session compression research (trajectory_compressor.py)
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- Tool-call error analysis (existing telemetry)
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