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14 Comments

AI agents need incident trails, not just better prompts.

OpenAI recently announced a framework for regularly disclosing unexpected or unauthorized model behavior, including several previously observed cases.

What's interesting here is the shift in thinking:

We're moving from

"How do we prevent every unexpected behavior?"

toward

"How do we detect, investigate and understand what happened?"

For agent systems, that naturally raises another question:

Can we trace the action itself?

That's one reason we're pushing Aegisora toward runtime evidence in 3.0.

A decision should not disappear after execution.

There should be a relationship between the request, the decision, the execution and the resulting evidence.

Aegisora GitHub

on September 23, 2026
  1. 1

    Nice work shipping it. What has been the biggest challenge since launch?

  2. 1

    Interesting approach. What was the hardest part to get right?

  3. 1

    Nice progress. What is the next thing you are focusing on?

  4. 1

    Nice work shipping it. What has been the biggest challenge since launch?

  5. 1

    Nice work shipping it. What has been the biggest challenge since launch?

  6. 1

    Have early users identified incident investigation as an urgent recurring problem, or is demand for runtime evidence still primarily driven by teams designing agent infrastructure?

  7. 1

    The execution boundary is the key insight. Prompt quality is hard to audit after the fact, but a durable trail lets you replay the request, decision, tool call, and result. That makes incident review useful instead of guesswork.

  8. 1

    Nice work shipping it. What has been the biggest challenge since launch?

  9. 1

    Nice work shipping it. What has been the biggest challenge since launch?

  10. 1

    This is great work — what's the biggest thing you'd do differently if you started over?

    1. 1

      I’d define the execution boundary earlier. We initially focused more on model behavior; the bigger challenge turned out to be controlling what happens after the model decides to act.

  11. 1

    This is great work — reminds me of some of the calls I've had to make building Xstream4K. What would you do differently if you started over?

    1. 1

      I’d design the evidence trail from day one. Retrofitting observability later is painful — keeping request → decision → execution connected from the start makes a huge difference.

  12. 1

    This resonates a lot — how long did it take before you saw any real signal on it?