Memory and dreaming

After a year of trying special-purpose memory tools, the production recipe described at the conference is plain: markdown files in a directory, agents searching them with ordinary tools, autonomy on writes, a fast index. What made it work at fleet scale were the guardrails around it — versioning with provenance (which session motivated this change, and who made it), concurrency control on writes, and permission tiers from read-only shared knowledge down to per-agent scratch space.

Two structural limits: split focus (an agent doing a task and curating memory simultaneously does both worse), and a visibility ceiling — a session only sees itself, so a failure pattern that recurs across the fleet is invisible to every individual agent. The fix is a second-order process that runs out of band, with its own budget.

An orchestrator deploys sub-agents over a batch of session transcripts — tool calls included, not just chat — and looks for prevalent patterns: repeated failures, missing knowledge, misconfigured tools. It then proposes individual memory edits, each citing the transcripts where the pattern occurred and how often it occurred. A human accepts or rejects each proposal. Nothing lands unreviewed. One worked example: every session failing the same way traced to a tool configured in radians instead of degrees — a fix visible only in transcripts.

Capture-then-distill pipelines that hash-chain sessions and mine failures and successes into draft skills a human reads before shipping. Reviewed, confidence-scored knowledge units that graduate from personal to team to shared, with agents validating a unit before acting on it and confirming or flagging it after. Promotion-based curation: capture everything privately, promote the rare dense result outward. And attributed mistake diaries, replayed as regression tests — one team turned a failure recurring in two-thirds of runs into a consistent pass by loading the lessons.

Per-project memory lives as markdown with a one-line-per-entry index — the filesystem recipe, arrived at independently. The operating rule "refresh, don't remember" re-reads state every cycle rather than trusting recall, which is the visibility-ceiling answer applied at cycle scale.

The registers give fleet learning a durable, citable shape: per-entry record files, append-only, tamper-evident, linkable from anywhere. The conference's mistake diaries are the closest analogue — without the deletion detection.

The production implementations hand-roll hash-based compare-and-swap for concurrent memory writes. Assay's memory already lives in git, where a rejected stale write is the compare-and-swap, history is provenance for free, and a proposal arriving as a pull request inherits both the accept/reject decision and the human merge gate.

Assay's design for the consolidation pass — out-of-band transcript mining proposing evidence-cited memory updates through review — predates the conference but has not been built. What the talks changed is its status: three independent reference implementations now exist, which converts it from a research bet into a build item. Two requirements the references added to the design: keep attribution through distillation (the rendered rule must cite the incident that paid for it), and treat transcripts as untrusted input — they were mined from sessions that handled untrusted content, so proposals derived from them inherit that provenance.

8 of 38 talks shown — the ones that reach the most concepts in this theme. Every sighting, per talk, is on the concept pages above.