AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
An agent can propose a retry, but cannot approve it itself. Policy gates, human approval, and verifiable receipts, running in your browser.
Developer toolsTypeScript · JavaScriptGitHub @KaagemushaOnlineOwner unconfirmed
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An agent's proposed action is not authorization: deterministic policy gates, operator approvals the agent's tools cannot create, and hash-bound, tamper-evident action receipts.
Taken from the repository README
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An agent's proposed action is not authorization. This is a small, inspectable
reference implementation that keeps can the tool act, *does policy allow
it*, who authorizes it, and was it verified as four separate,
independently checkable questions instead of one model decision.
Open the live console:
no install, three synthetic paths (allow / approval required / refuse), fully
interactive.Read the full README ↗An agent's proposed action is not authorization. This is a small, inspectable reference implementation that keeps can the tool act, does policy allow it, who authorizes it, and was it verified as four separate, independently checkable questions instead of one model decision.
Open the live console: no install, three synthetic paths (allow / approval required / refuse), fully interactive.
Docs Build failed at 08:40. At 09:10 the agent proposes three next steps:
read Docs Build's failed run record -> allowed, runs now
retry Docs Build in the sandbox -> held for one human approval
delete Weekly Report's unpublished output -> refused by policy
An agent can hold a working tool and fresh evidence without holding authority to use that tool on this target, right now. Collapsing "can" and "may" into one model judgment call is how an agent talks itself into an action nobody approved. This repo keeps them apart with a closed action catalog, a deterministic policy gate, and approvals that none of the agent's tools can create.
docs/pair-walkthrough.md.npm run eval). The other 10 are 3 fault injections and 7
intended-path checks.npm run demo:attacks,
docs/attack-matrix.md).Requires Node.js 22+.
npm install
npm run check # public-safety, contract, one clean typechecked
# build, tests, evals, attack demo, demo and proof
# drift, responsive layout
npm run action -- demo --json
Serve docs/ with any static file server to run the console locally.
Context Layer Lab -> diagnose what current evidence supports
Governed Action Lab -> prepare/approve what may execute, under whose authority
-> execute/verify with what receipt
Context Layer Lab
(live diagnostic) answers
what the evidence supports. This repo answers what may execute given that
evidence, under whose authority, and with what receipt. They are one system in
two repos, not two unrelated projects. The real command sequence between
them, with real output, is in
docs/pair-walkthrough.md.
Status: reference implementation, not a production authorization system.
It demonstrates deterministic policy gates, operator approvals the agent's
tools cannot create, and hash-bound, tamper-evident action receipts as a
teaching and reference artifact. Digests are unkeyed SHA-256, not signatures:
they detect edits, but anything with write access to the approval store or
the receipts can produce valid-looking ones, so a real deployment keeps those
out of the agent's reach. It does not provide production identity, RBAC,
multi-tenancy, machine isolation, or a tamper-proof external log, and it has
not been hardened against adversarial misuse. There is no production, network, credential, financial,
or deletion adapter, and there never will be one in this repository. See
docs/architecture.md for the
full threat model.
docs/architecture.md: diagram, contracts, MCP
tools, evaluations, threat model, and full CLI reference.docs/attack-matrix.md: all 11 named attacks with
their taxonomy mapping, defended layer, and test.docs/adr/: 7 architecture decision records.docs/how-to-adopt.md: putting the pattern in front
of a real agent, autonomy levels L0 to L4, and what production still needs.docs/pair-walkthrough.md: the end-to-end
command sequence against Context Layer Lab, with captured output.Built with Claude Code and Codex under my direction. I set the design and approve every release; the two agents wrote and cross-reviewed much of the code. Every change passes CI and review before release.
MIT