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Friday, 28 August 2026 Reading Room

AI

Agent memory needs a review state, not just a retrieval score

Agent memory is usually framed as a retrieval problem: find the prior note, summary, task, or preference that appears relevant to the current prompt. That is necessary, but it is not sufficient for a system that is about to prepare a consequential next step. A remembered item can be relevant and still be the wrong thing to use. It may be stale, from the wrong scope, missing an approval, or unable…

dev.to ·

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Silent Regressions Have No Stack Trace: A Minimal Prompt Eval Harness

A prompt regression is the only production bug that never throws an exception and never writes to your error log. When you change a prompt, the API still returns 200, the JSON still parses, and the…

  • Prompt regression is silent defect without error messages
  • API returns 200 status, JSON parses properly, latency unchanged
  • Minimal test harness catches regressions missed by manual testing

·dev.to

Turn Agent Tool Calls Into Run Receipts

The fastest way to debug a failing agent is not a better prompt; it's a better record of what the tools changed. Chat logs capture what the model said. A run receipt captures what the workspace did.

  • Record run receipts after each tool call to prevent transcript loss.
  • Wrapper around tool-call boundary provides auditable action tracking.
  • Minimal implementation needs stable run ID, git workspace, and wrapper.

·dev.to

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