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What have I saved about building useful AI agents?

Your saves point to three recurring requirements: preserve durable context, expose small inspectable tools, and make every answer traceable to evidence. They also favor retries and explicit stopping conditions over opaque autonomy.

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Try the library.

Ship the narrowest version that can teach you something real.

Agents need durable context, inspectable tools, and good stopping conditions.

Activation is the moment a user experiences the core promise.

A visual explanation is a compressed model, not decoration.

Pricing is product design expressed in constraints.

Calm software makes the next action obvious and lets the rest wait.

Operational simplicity is a feature you keep paying dividends on.

Debugging improves when the system can tell you what it believed.

Most prioritization problems are actually missing-strategy problems.

The best developer experience removes uncertainty before it removes keystrokes.

Citations turn an AI answer into a navigable research artifact.

Customer language is an asset you can collect before you need the copy.

A test is most valuable when it protects behavior people depend on.

Writing clarifies an idea by forcing it to survive outside your head.

The strongest interface feels faster because it predicts intent.

Performance work starts with a representative trace, not a favorite theory.

Organization should make retrieval easier than capture.

A practical tutorial gives the learner a working mental model.

Tools for agents should be composable, observable, and safe to retry.

A startup idea gets better when expressed as a specific user's repeated pain.

What have I saved about building useful AI agents?

Give your saved ideas a second life.