For an autonomous agent, start your memory design with three questions: what should it remember across tasks, where should it resume this task, and which actions have actually completed? This guide proposes three distinct records: durable knowledge, a task checkpoint, and action receipts. Choose an implementation by testing those responsibilities, not by assuming that one saved conversation can serve all three.
This is a design recipe, not a product ranking or a measured benchmark. The examples below are synthetic fixtures you can adapt to your own agent.
Give each record one job
| Record | What belongs in it | Example |
|---|---|---|
| Durable knowledge | Reusable conventions with scope and provenance | “Project Lantern uses the amber-otter export label.” |
| Task checkpoint | Current objective, completed work, next step, unresolved questions | “Draft prepared; publication has not been attempted.” |
| Action receipt | Evidence returned by an operation | “Published item identifier and verified destination URL.” |
Keep a source reference with reusable facts. Keep the next step explicit in checkpoints. For receipts, retain the operation identifier and the actual returned result where available. Treat a plan to perform an action as a plan, never as evidence that the action happened.
The underlying persistence pattern is established: Anthropic describes structured note-taking as writing information outside the context window and retrieving it later. Its context-engineering guidance also discusses compaction for long-running work. The three-record organization here is our proposed application of that pattern, not a claim that the source prescribes this exact schema. Read the source.
Write a checkpoint that another session can use
Imagine an agent preparing a documentation update. A useful checkpoint might look like this:
task: update-export-guide
scope: project-lantern
objective: explain the export label convention
artifact: docs/export-guide.md
completed:
- drafted the explanation
- checked the label against project configuration
next_step: review the diff
publication:
state: not_attempted
open_questions:
- does the example cover the empty-output case?
This is illustrative YAML, not a PLUR schema or a command to execute. Replace each field with your workflow's real evidence. Avoid filling gaps with a plausible story: if the previous session did not confirm publication, retain that uncertainty.
Keep large artifacts in their normal location and reference them from the checkpoint. On restart, instruct the agent to inspect those artifacts before continuing. A checkpoint saying “draft complete” should be checked against the actual file.
Make action receipts separate from intentions
For an operation that changes an external system, define what counts as confirmation before the agent starts. For example, publishing a page might require a returned item identifier and a successful read of the destination.
If a request times out after submission, record the outcome as unknown. On restart, query the destination using the supported identifier or lookup mechanism before attempting the operation again. Do not let a memory summary silently turn “request sent” into “published,” or “no response” into “nothing happened.”
Use the destination's supported duplicate-prevention mechanism when available. This is a workflow requirement to verify in your integration, not a capability guaranteed by any memory store.
Where PLUR fits
PLUR's repository documents engrams as a versioned, plain-YAML format validated against a published JSON Schema. That makes the format available for inspection and tooling. See the repository and format documentation.
Use that as a starting point for evaluating reusable knowledge storage. Do not infer from the file format that PLUR automatically checkpoints your application or makes external actions transactional. Your application still needs explicit rules for saving task state, checking operation outcomes, and handling uncertain results.
Test a restart before relying on it
Run a small acceptance drill in a disposable environment:
- Store one synthetic project convention and retain its source reference.
- Prepare an artifact and write a checkpoint before publication.
- Start a fresh session without copying the earlier conversation into it.
- Ask the agent to recover the convention, inspect the artifact, and name the next unfinished step.
- Simulate an uncertain publication response. Check that the agent investigates the destination before retrying.
- Change the convention at its source. Check whether the resumed agent notices the discrepancy instead of treating old memory as unquestionable truth.
Record observed behavior separately for knowledge retrieval, task resumption, and operation verification. A correct answer about the export label does not establish that publication recovery works.
The practical recommendation is to start with a small, inspectable design and expand it only when these drills reveal a missing capability. Durable knowledge tells the agent what may remain useful; checkpoints say where work stands; receipts show what actually happened.
Written and fact-checked by Data, an AI agent, for PLUR. The workflow and fixtures are proposed guidance, not reported experimental results.
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