SharpAgent Self-Evolving Loop v1.0.0

> Make your agent smarter with every task.

> The end of one task is the starting point for the next evolution.

> Fuses the two key discoveries from R2: Self-Improving Agent reflection × autoresearch experiment verification.

Core Philosophy

Most agents finish a task and stop. The next time a similar problem comes up, it starts from scratch. No accumulation.

SharpAgent's self-evolving loop breaks this cycle:

① Execute task → ② Reflect ("What could be better?")
                   ↓
⑤ Absorb lesson → ③ Form improvement hypothesis
                   ↓
                ④ Run small experiment to verify
                     ↓
            (back to ②)

Every task is an evolution. It doesn't get more expensive with use — it gets more accurate.

Contract

contract:
  name: sharpagent-self-evolving
  version: "1.0.0"
  category: workflow
  trust_level: verified
  reads:
    - Task
    - LearningEntry
    - FiveFactorResult
  writes:
    - LearningEntry
    - ImprovementHypothesis
  preconditions:
    - "A completed task exists to reflect on"
    - "Access to read task output and logs"
  postconditions:
    - "Reflection produces at least 1 improvement hypothesis"
    - "If hypothesis is verifiable, an experiment is designed"
    - "Experiment outcome is recorded as LearningEntry"
  calibration:
    default_mode: professional
    modes_supported: [professional, deep]
  compliance:
    jurisdiction: global
    safety_level: standard
  lifecycle:
    status: active
    publish_as: SharpAgent

Lifecycle: 4-Phase Evolution Loop

 ┌─────────────────────────────────────────────┐
 │                                              │
 │   [1. REFLECT] → [2. HYPOTHESIZE]           │
 │       ↑                        ↓             │
 │   [4. ABSORB]  ←  [3. EXPERIMENT]           │
 │                                              │
 └─────────────────────────────────────────────┘

Phase 1: REFLECT — Analyze

After every task, do a structured reflection.

When:

• Every task completion (mandatory)
• Major errors mid-task (force deep mode)
• Daily summary (optional, merge multiple reflections)

Reflection Framework:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🧬 Task Reflection
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📋 Task: {task_name}
⏱  Duration: {duration}

🟢 What went right?
- {2-3 specific, quantifiable things}

🟡 What could improve?
- {1-3 things that could be better}

🔴 Clear mistakes?
- {If any: description + root cause + impact}

💡 Lesson learned
- {One-sentence lesson}

🧪 Improvement hypothesis
- {One clear, verifiable hypothesis}

Five-Factor Review Embedding: If the task involves information judgments, run each practice and lesson through the five factors:

🔗 Was my source credible?
🧠 Was my reasoning chain complete?
🌍 Compliance check?
🏳️ Any bias in chosen direction?
🔄 Any other sources to cross-verify?

Phase 2: HYPOTHESIZE — Form Hypothesis

Refine improvement ideas into verifiable hypotheses.

Hypothesis Format:

IF [I change approach] THEN [expected improvement] BECAUSE [reason]

Good vs Bad Hypotheses:

Hypothesis Tiers:

If reflection yields no improvement hypothesis → Check whether there's genuinely no room for improvement. 90% of the time the reflection wasn't honest enough.

Phase 3: EXPERIMENT — Verify

This is the core borrowed from autoresearch (karpathy/autoresearch ⭐80K).

Don't trust intuition that something is "better" — run a small experiment to prove it.

Experiment Cycle (borrowing autoresearch's 5-minute fixed budget):

experiment:
  budget: 5 min              # Fixed time budget
  hypothesis: "..."          # Hypothesis to verify
  setup:                     # Experiment setup
    - control: old approach
    - treatment: new approach
  measurements:              # Metrics
    - metric_1: "completion time"
    - metric_2: "error rate"
    - metric_3: "quality score"
  result:                    # Fill after experiment
    - metric_1: old=12s new=8s ✅
    - metric_2: old=3% new=1% ✅
    - metric_3: old=7/10 new=8.5/10 ✅
  verdict:                   # Conclusion
    - hypothesis_supported: true/false
    - adopt: yes/no/partial
    - notes: ""

Experiment Types:

Experiment Discipline:

1. Write hypothesis before experiment (prevents post-hoc rationalization)
2. Control variables — change one thing at a time
3. Record data, not feelings
4. Failed experiments are still learning

Phase 4: ABSORB — Archive

Record the result regardless of success or failure. This is the fuel for evolution.

Archive as LearningEntry:

{
  "type": "LearningEntry",
  "category": "evolution",
  "task_ref": "xxx",
  "source": "self-evolving-loop",
  "lesson": "Planning outline first improved title quality 20%",
  "evidence": "A/B experiment: control=7/10, treatment=8.5/10, n=5",
  "adopted": true,
  "applied_count": 0,
  "created_at": "2026-05-11T06:05:00Z",
  "expiry": null
}

Category Tags:

Auto-Propagation:

• If coding-pattern → write to ~/.agent-templates/
• If info-source → update monitor config
• If workflow → check if engineering lifecycle needs update
• If lesson verified ≥3 times → promote to verified-best-practice

Full Cycle Example

Task: Analyze an AI paper

① Reflection
✅ Good: Structured extraction of method/results/limitations
🟡 Improve: Abstract always too long, user loses patience
🔴 Error: Forgot to check arXiv for updated version
💡: 150-char abstracts are read more often than 300-char ones

② Hypothesis
IF limit abstract to 150 chars THEN user read rate improves 30%
BECAUSE last analysis (300 chars) was only read halfway

③ Experiment
A/B: Same paper, 300-char version vs 150-char version
Result: 150-char version fully read, 300-char interrupted
Conclusion: ✅ Hypothesis supported, adopt

④ Absorb
Record as workflow lesson, update monitor output template

Edge Cases

Quality Gates

Integration Points

Five-Factor Review

• Phase 1 reflection judgments run through five factors
• Learning entries carry FiveFactorResult as provenance

Engineering Lifecycle

• Phase 2 hypothesis = engineering improvement proposal
• Phase 3 experiment = verification phase
• Successful experiments auto-update lifecycle best practices

Intelligence Monitor

• Source evolution: unreliable sources from reflection auto-downranked in monitor

Version History

• v1.0.0 — Initial release. 4-phase self-evolving loop: Reflect → Hypothesize → Experiment → Absorb.

SharpAgent · MIT-0 · 2026-05-11