The living record

What changed,
and why.

An answer can change. So can the evidence behind a workflow. Follow the revisions, the Briefs that connect them, and the tests we actually ran.

How to read this record

This page draws on reviewed versions in the current AI collection. It includes revisions, Briefs and Playbook test records; new Explainers remain in Questions. Dates below are when Atlas recorded and reviewed a version, not necessarily its first publication date. An editorial revision does not by itself mean the underlying facts changed.

Titles and reasons come from the saved versions; the title link opens the current article. Test dates refer to execution. A new review or a changed deadline is not a new test. Brief links state which article revision they address and disappear from current advice if that revision is replaced or unavailable. The Atom feed uses the same reviewed entries.

All changes

  1. Revision 2
    Editorial revision

    When should an AI agent stop trusting its memory?

    Links the freshness follow-up to the experience-learning Explainer and states what it adds. The experiment, findings and limits are unchanged.

    Builds on this answerWhen does an AI agent actually learn from experience? →

    The learning comparison asked whether a stored experience improved later results. This follow-up asks which parts of a saved work record still apply after the task or source changes. It tests a different question and does not rerun the original comparison.

    For the reader. When reusing a work record, check the source, version and scope behind each conclusion. Preserve valid work and explain what needed a new check; these results do not establish a benefit from extra memory.

  2. Revision 2
    Revision

    Keep Codex on track: a workflow for long tasks

    Clarified native Codex resume, sharpened the title, brought the steps forward and added file excerpts showing what the changed assignment required. The recorded test is unchanged.

    Existing test retained. Last run: . This revision adds no new execution evidence.

  3. Revision 1
  4. Revision 1
  5. Revision 2
    Editorial revision

    When does an AI agent actually learn from experience?

    Clarified the baseline ceiling and learning definition; simplified results and moved the explanatory loop to the opening.

    Compare the takeaway

    Before

    Give experience a route into future work, and a way to be rejected. Adopt a new method when checked outcomes justify it; keep the baseline when they do not.

    In this version

    An agent learns from experience when it uses past outcomes to change how it handles a new task. Whether that change helps needs a separate check.

  6. Revision 6
  7. Revision 2
  8. Revision 5
    Revision

    Why would an AI agent choose your company?

    Refines the opening, headings and prose for clarity and flow; preserves claims, sources, examples and limitations.

    Compare the takeaway

    Before

    An agent chooses from the market it can see, using the evidence and customer context available to it. Your advantage must survive that translation—and being the recommended brand still may not make you the chosen seller.

    In this version

    An agent chooses from the market it can see, using the evidence and customer context available to it. Your advantage needs to remain visible through that process. Even then, being the recommended brand may not make you the chosen seller.

  9. Revision 4
    Revision

    Why would an AI agent choose your company?

    Explains how agents choose companies; moves the unchanged tested drafting pilot into its own guide.

    Compare the takeaway

    Before

    Prepare on two fronts: make your offer dependable when customers use agents, and improve one complete workflow inside the company. Invest further only when customer value, economics and control hold up.

    In this version

    An agent chooses from the market it can see, using the evidence and customer context available to it. Your advantage must survive that translation—and being the recommended brand still may not make you the chosen seller.

  10. Revision 3
  11. Revision 2
    Revision

    What should brands and companies do about AI agents?

    Adds strategic choices for customer access, differentiated value, workflow economics and delegated authority. The original four-step pilot and its test remain unchanged.

    Compare the takeaway

    Before

    Keep customer facts accurate, give one workflow clear limits, and inspect the result before expanding delegation.

    In this version

    Prepare on two fronts: make your offer dependable when customers use agents, and improve one complete workflow inside the company. Invest further only when customer value, economics and control hold up.

  12. Revision 4
  13. Revision 3
  14. Revision 2

That is the recorded history. Find a question to explore →