Most AI tools keep a chat history for you to read. The cognitive ledger in hmharness — an open-source (MIT) agent framework for HarmonyOS/OpenHarmony development — exists for a different reader: the auditor.
15 categories of cognitive events (predictions, verifications, revisions, decisions, learning signals...) are append-only, never deleted, and each carries a lineage chain: which belief this prediction came from, which verification revised it, what descended from that revision. The whole cognitive process forms a replayable DAG.
Why append-only
History that can be edited after the fact is not history. If an AI can silently rewrite its records, what you see is a cosmetically improved decision chain — errors never admitted, revisions never traced. Immutability buys something expensive: accountability. Any wrong prediction can be traced along its lineage to the belief that produced it; every revision has a paper trail.
Ledger 2.0
Five new event categories, plus beliefVersion (versioned beliefs) and descendants (derivation chains) — the cognitive DAG becomes fully replayable.
Unified provenance prevents silent cheating
Canonical lineageId: conclusions from different experiments on different axes are not comparable unless their lineage matches. "Using data from experiment A to argue for conclusion B" is blocked by rule, not by goodwill.
What this means for developers
Every conclusion the agent gives you carries a birth certificate. When it says "this signing configuration is correct", you can ask the ledger: which verification produced this? What environment was used? Was it ever overturned — when, and on what grounds?
The AI stops being a black box that speaks from memory, and becomes an executor with a complete medical record.
The philosophy in one line
The web workbench guarantees "the numbers you see are traceable"; the ledger guarantees "what the AI knows is replayable". Two sides of the same principle: nothing inside the agent is unverifiable.
hmharness wires project creation, checks, builds, signing, install, launch, log reading, and result verification into a local toolchain so AI does not just generate code — it proves results against the local machine. Independent community project, no affiliation with Huawei or OpenAtom.
GitHub: https://github.com/swsgbl/hmharness · Evidence: https://swsgbl.github.io/hmharness/evidence/aiobservabilityopensourceaudit
Top comments (1)
Blocking "data from experiment A argues for conclusion B" by rule instead of goodwill is the strongest idea here. I'd add one thing: when a verification gets overturned, flag everything that descended from it for a fresh look, not only the conclusion itself. We keep a shared ledger at ML Systems on the same append-only rule.