@svpino The idea of a product that compounds in value the more it's used is such a powerful mindset shift. Building systems that learn from real usage is where the durable magic happens. This is the kind of thinking that separates good tools from great ones. Loved this breakdown!
@svpino The underrated part is #4. A learning loop is only a moat if the memory boundary is explicit: user facts stay private, reusable workflows become product knowledge, and failed traces become tests. Otherwise you don't build a moat. You build context leakage with better branding.
@svpino That's a really smart approach. Making agents better with use is a game changer.
@svpino This is very valuable info ☝️
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:
Many users praised self-learning agents building moats via user traces and fixes as valuable and game-changing, while a few noted that effective implementation is harder than it seems.
Based on 9 visible X reactions from 27 accounts; directional sample.
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