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A system can save notes successfully and still have no memory in practice. The key question is whether later planning or execution retrieves those notes and uses them. In a September 16, 2026, case report, software author Michael Hairetis found that his agents wrote per-job notes that were only archived or listed—not read back into their work.
What went wrong in the reported system?
Hairetis describes an orchestration platform that ran scheduled data jobs through worker agents. Each job’s agent was told to preserve durable techniques in a per-job notes file, such as parsing pitfalls, job cadence, and known-good ways to access data. Some files grew to about 40 KB.
When Hairetis searched the codebase for the notes filename, he found only two references: an archiving routine and a UI file-lister. Neither reference put the notes into a prompt or otherwise made them available during planning or execution. As he put it, “I had agents writing notes that nothing ever read.” Michael Hairetis’s September 16, 2026, DEV Community post
That is a write-only memory path: persistence exists, but retrieval and use do not. A file’s presence, size, or successful write confirms only that information was stored. It does not show that a later run can find, interpret, or act on it.
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How can you check whether persisted information is actually used?
Trace one saved item from the point it is written to the point a later run could benefit from it. Search for the filename, key, database table, or API endpoint that receives the data, then inspect what each read-side reference does. Hairetis’s practical advice was: “Go grep for the filename your agents write to.”
- Locate the write. Identify the code that creates or updates the persistent record, and note its format, location, and scope.
- Find every read reference. Search for the filename or storage key across the codebase. Include prompt construction, planning, job startup, and execution paths—not just user-interface code.
- Follow the data after it is read. Verify that the retrieved content reaches the component that makes decisions. A file listing, archive operation, or diagnostic display is not evidence that an agent uses the information.
- Check timing and selection. Determine whether retrieval occurs before the relevant plan or action, and whether the system loads the right job’s notes rather than unrelated or stale context.
- Test a durable fact end to end. Save a known technique, start a later run, and confirm that the relevant step receives or retrieves it. A write test alone cannot verify a read path.
In the case Hairetis reports, the two filename references led to archiving and UI listing, not to a later run’s planning or execution. That distinction is the debugging signal: storage operations can make memory look active while leaving the decision path untouched.
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Why does a missing read path cost agent turns?
Hairetis also describes a worker plan that disappeared when a run ended. A frequently run job therefore reconstructed its plan each day, including schema details, file paths, and rules for identifying data. The repeated work was not necessarily caused by a poor plan; stable information simply was not carried forward.
For the system he described, Hairetis reported five agent turns for a clean run. He reported successful runs taking an average of 6.5 turns and as many as 10, while runs that concluded there was no new data took an average of 3.5 turns. These are the author’s observations about his own system, not independent measurements or general benchmarks. They illustrate why measuring only whether a job eventually succeeds can miss avoidable reconstruction.
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How should persistent context be managed?
Reading everything back is not automatically the answer. Hairetis’s recommendations for his system balance reuse against limited context: give each run a bounded, curated set of useful information, while keeping further details available on disk when needed.
- Curate rather than append indefinitely. Update changed facts, remove superseded ones, and avoid rewriting notes when no durable information has changed.
- Keep the current plan in priority. When context is limited, trim supplementary notes before discarding current instructions or the plan for the run.
- Make retrieval purposeful. Put the most relevant stable facts in the run’s initial context and retrieve deeper detail only when the task needs it.
- Distinguish a quiet result from a failed plan. Tell the planner that “nothing new today” can be a valid outcome of a successful plan, rather than treating it automatically as a reason to rewrite the plan.
These are design practices Hairetis proposes from his experience, not a universal architecture standard. The useful test is whether the information available to a run is relevant, current, and sufficient without crowding out the instructions it must follow now.
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What to verify before calling it memory
- A later run can retrieve the saved information at the stage when it matters.
- The retrieved information reaches the planner or executor, not just a log, archive, or file browser.
- Repeated work reuses stable facts while changed or obsolete facts are updated or removed.
- Context limits preserve the current plan and instructions ahead of supplementary notes.
- A valid no-change result is recognized as a possible successful outcome.
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