Agenting AI — Reflection & Memory Prototype
An experimental notebook-based prototype exploring reflection-driven learning, structured error memory, and iterative reasoning for self-improving AI workflows.
The prototype models a loop in which an agent executes a task, detects failures, formalizes errors, stores memory, generates reflection, retrieves relevant past mistakes, and attempts an improved response.
It is intentionally research-oriented rather than a production deployment, focusing on architecture, memory, reflection, and adaptive reasoning concepts.
What the project demonstrates.
Structured error-memory workflow
Reflection-based reasoning loop
Memory retrieval for later task attempts
Experimental research prototype
How the work was approached.
The challenge
Explore whether a long-horizon AI agent can reuse prior failures rather than repeating the same errors.
Technical approach
- Capture failure patterns as structured error memory.
- Retrieve past mistakes and apply reflection rules when solving later tasks.
Evidence & outcomes
- Notebook-oriented prototype with an error-memory loop.
- Architecture illustrates reflection, retrieval and iterative retries.
Scope note: A research prototype; no production reliability or benchmark guarantee is claimed.
Want to inspect the implementation? Explore the source repository ↗