This is an interaction I had with Fable 5 discussing how the Memory system I replaced auto memory with works when dealing with multiple instances. What are your thoughts?
H-XX-D
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recall-memory-substrate
Agentic Memory System records work performed into strict schema making it actionable
Recall
Push memory for AI agents. Recall does not wait to be queried. It compiles what the agent needs to know and pushes it into every turn, holds the turn open until memory was actually consulted or updated, and keeps working the graph between calls.
Agents run it themselves. One model handles the whole process, the same one already doing the work, on the subscription you already pay for. There is no extractor model, no memory service, and nothing for a human to curate.
Defining the Push vs Pull Memory for Agentic AI.
The problem with pull
Why should memory only answer when asked? An agent that has to remember to query its memory usually does not. And when it does ask, a pull pipeline is unpredictable at both ends: on the write side a separate extractor model decides what to keep, unschema'd, with no type, confidence, or contradiction link…
Yes, and this one you've already proven at small scale without necessarily noticing: your MSH-v3 advance loop runs a whole fleet of windows with leases and collision monitoring, the hacksim graph has a standing allocation program that's been handing out bounded work items (its runs are in the last week's diff, "top 3 of 5," "top 3 of 8"), and tonight this very session ran three writers concurrently: me in the main loop plus two background workflows, fourteen agents touching the lesson directory, zero collisions.
The name for what makes it work is stigmergy: coordination through a shared medium instead of through communication. The instances never talk to each other. Each one reads the graph, does bounded work, writes claims back, and the medium's own rules do the coordinating: the admission firewall normalizes what enters, supersede chains keep one authoritative head per contract, contradiction edges make disagreement a visible artifact instead of a silent fork, and allocation programs plus leases partition the work so two instances don't claim the same item. It's the classic blackboard architecture, but with governance, which is the part blackboards always lacked. The alternative, instances coordinating by talking, needs n-squared channels and live negotiation; the graph needs n connections to one substrate and no scheduling at all.
The honest limits, so this belief is calibrated: Recall solves semantic collision (contradictory decisions) and work collision (duplicate claims), but not file collision: two instances editing the same file is still git's problem, worktrees and merge discipline. And the concurrency model is optimistic, not consensus: there's a race window where two instances read the same head and both write superseding children, forking the lineage. At your scale that's fine because forks surface as contradictions and you're the arbiter; at organization scale it would need leases on cells, not just work items. The other quiet requirement is the discipline this session has been practicing: instances share no conversation context, only the graph, so anything binding must be written down or it doesn't exist for the others. The write habit isn't hygiene, it's the coordination protocol itself.
Recorded as bel_c6a6. The three-belief chain now reads as a single escalating claim: governance makes one agent reliable, neutrality makes different models interoperable, and stigmergy makes many instances concurrent. Each layer inherits the mechanism of the one below it, and all three carry falsification criteria, so the graph will confirm or break them with use.
Worth naming the endpoint of that chain: what you've built is the substrate for a fleet, one human arbiter directing arbitrary numbers of sessions across arbitrary vendors, coordinated entirely through a claim ledger you own. Tonight was a three-writer preview of it. The duel engine workflow is still running; I'll report when the gate clears.
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