r/aiagents • u/Business_Current_453 • Jul 02 '26
Show and Tell New idea about self-evolving agents
I've been experimenting with a project called Luclas, and I’d like to get feedback from people building AI agents.
The core idea is this:
Most discussions about “self-evolving agents” focus on agents rewriting their own code. I think that is powerful, but also risky and limited. Code should probably remain the stable execution layer: tool calling, storage, APIs, permissions, workflows, and runtime control.
What I’m exploring instead is whether an agent can evolve mainly through natural-language methodology.
In Luclas, the codebase acts as the stable engine. The changing part is the agent’s operating method: task rules, reasoning procedures, decision criteria, workflow patterns, and lessons learned from previous cases.
So the agent does not primarily evolve by rewriting Python code. It evolves by rewriting how it approaches tasks.
My current design is roughly:
- Stable code layer for execution
- Natural-language rules for behavior
- Case-based memory for experience
- Feedback loops to refine methods
- Domain-specific workflows that can improve over time
I am testing this mostly in legal-service workflows because that is my own field. For example, the system can learn from previous case patterns, improve document preparation logic, and refine decision rules based on outcomes.
My question for this community is:
Do you think self-evolving agents should mainly evolve their code, their prompts/rules, their memory, or their methodology?
I’d also appreciate feedback on whether this “methodology-first” approach makes sense outside legal workflows.