@Saboo_Shubham_: Static agents won't survive 2026. Self-learning agents are WHAT you need, and the best ones get better every time someo…
Summary
A discussion on how AI agents learn in three layers (model, harness, context) and why capturing user corrections as labeled examples is key to building self-learning agents that improve over time.
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Static agents won’t survive 2026.
Self-learning agents are WHAT you need, and the best ones get better every time someone uses them.
Let’s understand this.
Your AI Agents actually learn in 3 places:
-
Model (the weights): owned by the AI labs, the hardest layer to improve.
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Harness (the loop, tools, prompts): medium difficulty. You can improve it, but it takes some real work.
-
Context (memory + skills): plain text you can read and edit, the easiest place to start.
You already use all three in Claude Code and Codex.
But here’s what almost everyone skips:
Your agent should learn from your users, not just its own runs.
Every time a person fixes the agent’s call, that’s a labeled example no benchmark can give you. Worth more than a hundred synthetic tests.
Capture it. Feed it back. That’s the loop that compounds.
yess
Memory with taste is what makes the difference
@Saboo_Shubham_ been saying this! static agents just can’t keep up. it’s gonna be all about those self-learners soon.
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