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18 notes

Notes on AI systems

Code, traces, and experiments about agents, language models, and the software systems around them.

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17. Running a Fault-Injection Exercise for an Agent

A successful happy path proves that the system can run. A controlled failure shows whether it knows where to stop and how to recover.

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16. Building an Agent Regression Suite Before Release

A regression suite is not a rerun of a few demos. It is an executable record of failures the system must not repeat.

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09. Agent Permission Is More Than Allow or Deny

Authorization decides whether one principal may perform one action on one resource under specific conditions

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07. Are Parallel Tool Calls Actually Faster?

Concurrency shortens only independent waiting time and adds result joining, cancellation, and resource pressure

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12. Context, Summaries, and Long-Term Memory Are Different

Longer retention does not create stronger knowledge; unverified memory carries old errors into new tasks

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15. Evaluating an Agent Beyond Its Final Answer

A plausible answer does not prove that evidence is real, execution was authorized, or interruption recovery is correct

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01. How System Prompts Are Assembled

The model does not receive one prompt. It receives context blocks assembled from different sources for different purposes.

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06. How Tool Results Should Return to the Model

The model does not observe local execution; the runtime must carry evidence into the next turn