How I Built a Self-Improving AI Agent That Evolves Its Own Mind

"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:

1. Draft an initial plan (prompt)
2. Execute it using the local LLM
3. Evaluate outcome quality
4. Rewrite plan if performance is subpar
5. 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 🚀