"A walkthrough of designing an AI agent that rewrites its own strategies using recursive optimization, inspired by meta-learning and AGI research."
What if an AI could improve itself without external supervision? That question became the seed for this project.
🧠 The Goal
Build a recursive self-improving agent — a system that:
- Writes its own prompts
- Evaluates and critiques its past runs
- Updates internal strategies autonomously
- Learns over time via feedback loops
Inspired by meta-learning, recursive self-reflection, and AGI architecture principles.
🛠️ Tech Stack
| Component | Tools/Approach |
|---|---|
| LLM Engine | Ollama (local inference) |
| Evaluation Logic | Chain-of-Thought + Self-Critique |
| Memory | JSON logs + vector database |
| Planning | Dynamic Prompt Rewriter |
| Tuning | Self-generated hyperparameter sweep |
🔁 Self-Improvement Loop
The core of the agent is a recursive reasoning loop:
- Draft an initial plan (prompt)
- Execute it using the local LLM
- Evaluate outcome quality
- Rewrite plan if performance is subpar
- Retry and compare outcomes
This loop continues until a threshold of self-satisfaction is reached.
[Plan] → [Run] → [Critique] → [Update Plan] → [Repeat]
🧪 Key Capabilities
🧩 Self-rewriting prompts: Agent modifies its own logic mid-task.
🎯 Performance-aware optimization: Adjusts strategies based on reward or error signals.
🗃️ Memory persistence: Learns over sessions by storing and referencing past runs.
💡 Emergent reasoning patterns: Shows signs of internal deliberation and experimentation.
🌱 Why This Matters
Most AI agents are static. But real intelligence — human or artificial — is dynamic, reflective, and adaptive. This project is a step toward AGI systems that can:
Grow over time
Adapt to new challenges
Optimize themselves without hardcoded updates
📚 What's Next
Multi-agent dialogue: Let multiple internal agents debate and vote
Goal generalization: From task-specific to goal-agnostic optimization
Ethics layer: Align improvements with human feedback
📂 Open Source
I’ll be open-sourcing the full codebase + research notes soon.
Follow me or drop a comment if you’re interested in contributing or testing the system.
Thanks for reading — and if you're building something similar, I’d love to connect 🚀