@svpino: How you can build a moat with self-learning agents: If you can build an agent that gets better every time people use it…

How you can build a moat with self-learning agents:

If you can build an agent that gets better every time people use it, you will be unstoppable.

Here is what you can do:

1. Learn from two sources, not one. Agent traces show what the agent did and where it broke. In-browser activity shows how users steered and fixed the results. Most products capture the first source and forget the second.

2. You have three options to apply new learnings: fine-tune your model, update the harness, or provide in-context information to the agent. Make sure you take advantage of all three.

3. When deciding what your agent should remember, lean on procedural memory (where you store workflows and rules) and episodic memory (where you store specific things that happened). Don’t over-index on semantic memory, as it can go stale and become a liability.

4. Set boundaries around how far learning spreads (per-user, per-team, per-app). Be careful not to leak facts between different users’ contexts.

5. You want to own the data your agent learns from. This data is your most valuable asset. Try to keep it in your infrastructure so it doesn’t get stolen by Big Cloud.

6. Use open, framework-agnostic standards to capture and apply learnings.

Here is an article with more information:

@svpino honestly, i’m just trying to get my own code to not break most days . But this idea of self-learning agents is wild. do you see any easy wins for beginners?