Metis: Governed Tacit Memory for AI Agents

Capture how expert work actually gets done, govern it, and serve it to AI agents as memory they are allowed to use.

AI agents now act inside real workflows, but the knowledge that makes work go right was often never written down. A technician hears a pump sounds wrong before any alarm. An inspector sees a batch "looks off" before the lab confirms it. Manuals do not capture this, and naively mining it from workers is unsafe and easy to get wrong.

Metis is a local-first Python toolkit that captures these moments as governed tacit fragments, has a human group validate them, and serves only the validated ones to an AI agent, under the exact conditions where they hold, with a full audit trail. It implements the governed tacit-memory layer from the paper Tacit Fragments: Operationalising Tacit Knowledge as a Governed Memory Layer for Agentic AI, and it runs on CHAP so every step is recorded on a hash-linked, replayable evidence chain.

Architecture diagrams, an interactive demo, and an illustrated explainer live in the GitHub repository.

What is a tacit fragment?

Most of what makes someone good at their job never reaches a document. A tacit fragment is a small, structured, governed record of one such piece of practice. It is deliberately partial: it is not a worker's whole expertise, and it is never treated as fact.

Every fragment carries the things that make it safe to reuse:

• what was observed and worker-confirmed, and its category (the K1 to K17 taxonomy of tacit knowledge),
• the conditions under which it applies (site, equipment, operating mode, shift, role, risk, and exclusions),
• where it came from: provenance, the worker, and their consent,
• the evidence behind it (recurrence, supporting cases, counterexamples),
• an authority layer: Evidence (learning only), Advisory (conditional guidance), or Controlled (formal instruction),
• use constraints that travel with it.

That structure is the point. A document chunk has no conditions, consent, or authority, and a training example is treated as ground truth. A tacit fragment is neither. It is situated guidance an agent may use only where it applies, and must stop using the moment it does not.

A concrete example

A pump SOP says: reduce load only when the alarm threshold is crossed. Experienced operators reduce throughput earlier, when high load coincides with low-frequency vibration and a dull acoustic cue. Metis captures that gap, a human group promotes it to an advisory cue, and an agent can then use it, but only on the right pump in the right state.

from metis import MetisEngine
from metis.conditions.context import TacitContext
from metis.consent.model import ConsentRecord, ConsentStatus

eng = MetisEngine()              # local and deterministic, no cloud APIs
eng.join_default_participants()

# Capture what the operator does that the SOP does not say.
frag = eng.capture_observation(
    {
        "observation_id": "OBS-1",
        "work_as_imagined": "Reduce load only when the alarm threshold is crossed.",
        "work_as_done": "Reduce throughput earlier on low-frequency vibration and a dull acoustic cue.",
        "context": TacitContext(equipment_family="centrifugal_pump", operating_mode="high_load"),
    },
    consent=ConsentRecord(consent_status=ConsentStatus.granted),
    category="K7_sensory",
).fragment                            # lands in the Evidence layer, not yet usable

# A human Mission Group promotes it. A model never makes this call.
eng.tier2_review(frag.fragment_id, "promoted_to_advisory", summary="advisory cue only")

# An agent asks for guidance. The gate returns it only when the context matches.
match = TacitContext(equipment_family="centrifugal_pump", operating_mode="high_load", risk_class="moderate")
other = TacitContext(equipment_family="gear_pump", operating_mode="low_load", risk_class="moderate")

print(len(eng.retrieve(match).eligible))      # 1  (returned, with its use constraints)
print(eng.retrieve(other).blocked[0].reason)  # conditions_do_not_match

Change the pump, raise the risk class, or withdraw consent, and the same fragment is withheld with a recorded reason. Retrieval is a governance decision, not a similarity search.

Quickstart

No cloud and no GPU. The demo and tests run without any model using deterministic fixtures.

git clone https://github.com/BrightbeamAI/metis && cd metis
pip install -e .
metis demo manufacturing-pump-vibration

The installable package name is metis-memory (import metis, CLI metis). The official chap-coordinator dependency installs from PyPI automatically.

The demo runs the whole flow locally and writes a replayable evidence chain. Inspect it with metis fragment list, metis memory list, metis retrieve --context <file>, and metis audit read.

Prefer to click through it? Open the interactive demo: pick a scenario, step through the loop, and drive the gate yourself by editing the context and watching it allow or block. For a guided tour, open the illustrated explainer.

How it works

Capture loop. Observe a work event, infer a candidate (a hypothesis, never trusted), whisper one short bounded question to the worker, confirm with them (descriptive fidelity only), and remember the result as an Evidence-layer fragment.

Governance. A human Mission Group reviews each fragment across fidelity, operational relevance, normative alignment, and risk, then promotes it to Advisory or Controlled, or holds, rejects, or re-elicits it. Evidence-layer fragments can never drive a decision or reach an agent. A local model may draft a review summary, but it never decides.

Memory and retrieval. A promoted fragment becomes a governed memory object. A broker assembles an agent context from procedural, semantic, episodic, and tacit memory, and tacit memory is reached only through the condition-aware gate, which carries the use constraints with it.

Learn more

• Interactive demo and explainer: the fastest way to get it.
• Documentation: architecture, governance, retrieval, the K1 to K17 taxonomy, agent use.
• ABOUT.md: repository map, the four memory stores, the CHAP relationship, and how to develop.
• CHAP: the protocol Metis runs on.

Ethical use

Metis captures fragments of human work. Do not use it for covert worker monitoring. It records no audio, video, biometrics, screenshots, or keystrokes, fragments are never treated as fact, and the audit chain is append-only. Production use needs worker consultation, legal review, and domain validation. Read ETHICAL_USE.md first.

License

Apache-2.0. See LICENSE.

Citation

Metis is the reference implementation for the paper Tacit Fragments: Operationalising Tacit Knowledge as a Governed Memory Layer for Agentic AI (preprint, also included in this repository as docs/tacit_fragments_preprint.pdf). If you use Metis in research, please cite:

@article{shahid2026tacitfragments,
  title   = {Tacit Fragments: Operationalising Tacit Knowledge as a Governed Memory Layer for Agentic AI},
  author  = {Shahid, Arsalan and Suttie, Gordon and Black, Philip and Garz{\'o}n-Vico, Antonio},
  journal = {Preprints},
  year    = {2026},
  doi     = {10.20944/preprints202608.0927.v1},
  url     = {https://www.preprints.org/manuscript/202608.0927}
}