Can an AI agent remember a workflow after its runtime disappears?
This is a portfolio sample built from a real hands-on test. It shows the shape of a small paid Romo7 Research Sprint: one focused question, source-linked evidence, a test, limits and an actionable recommendation.
Can useful agent state survive outside the chat session?
The practical requirement was simple: if a computer disconnects, cloud-safe work should continue and device-dependent work should resume later without asking the user to repeat the task.
The test focused on whether that rule could be stored outside the active model process and recovered by a later runtime.
A small experiment designed to fail cleanly.
- 01
Installed the official Lighthouse Memory SDK and used the batched engine with a local filesystem adapter.
- 02
Stored one real workflow rule with
remember()and forced the batch to flush. - 03
Recorded the returned content-addressed identifier (CID), then ended that Node process.
- 04
Started a separate process with no chat transcript carried over and asked what the agent should do after a computer disconnects.
- 05
The memory layer returned the stored workflow rule and the same CID.
What the test actually supports — and what it does not.
Use external memory for durable operational rules — not as an excuse to remember everything.
For agent workflows that need continuity, keep important operational state outside the active model session. Store only what is worth retaining, avoid secrets or sensitive data in plaintext backends, and test deletion semantics before production use.
The next production-relevant test is multi-device or remote-backend recovery: persist on one machine or client, restore from another, then measure retrieval quality, latency and deletion behavior.
What I would not claim from this test.
- No proof of decentralized-network persistence.
- No multi-device recovery test.
- No latency, cost or large-memory retrieval benchmark.
- One workflow rule, not a broad production dataset.
- Privacy and deletion behavior depend on the selected storage backend.
Public references used in the test.
One question. Sources. Evidence. Recommendation.
No meeting required. Written scope, a source-linked brief, evidence table and practical recommendation, with a target 48-hour turnaround after agreement.