AI DevOps
Execution governance for consequential AI-agent operations
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A platform that governs consequential AI agent actions by revalidating authority at the moment of execution and recording signed decisions that third parties
Rigorix provides execution governance for AI agents handling consequential operations such as financial transactions, database migrations, and production changes. It separates the moment of approval from the moment of execution, revalidates authority at dispatch time against live conditions, and records cryptographically signed evidence of every decision. The platform enforces multiple controls before an action executes: permission and authority checks, sequence and prerequisite validation, identity and effect matching, and evidence generation. Evidence is signed with HMAC, hash-chained, and can be anchored externally, allowing third parties to verify decisions without trusting the vendor. Rigorix deploys as a self-hosted engine (free and open source), with optional control plane services for policy management, audit logging, and cross-session capacity enforcement.
Pricing model: Freemium
Free engine (MIT/Apache-2.0 open source). Team tier: €1,900/month including 25 writing seats and 50,000 governed actions per month; overage €0.02 per action above 50k monthly. Enterprise: from €90,000/year. Design partner program: €18,000 for 90 days (credited against first annual contract). Add-ons
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The discipline of deciding again at the moment an action becomes an effect, and proving what was decided. It adds five commitments to a normal agent stack: a bounded plan, authority revalidated at dispatch, constraints on composition, ownership of the interval between dispatch and settlement, and evidence a third party can verify.
No. An approval is a human statement about intent. Rigorix binds that approval to the canonical intent and then rechecks the world at dispatch. The approval releases the pause; it does not pre-approve the world.
Yes. The verifier is a protocol-only package with no engine dependency. Download the sample bundle, run the verifier offline, and read the level it achieved. The page shows what each level proves and what it does not.
The step does not dispatch and the downstream effect does not happen. The refusal is a successful control: it is recorded as a signed finding with the rule, the step, the exit code, the input digest and the authority digest. A reviewer can reconstruct the refusal without a transcript.
It works with any agent that can call an MCP server, plus the CLI and the GitHub Action. The agent keeps its own loop; Rigorix is the layer it runs inside.
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Rigorix has a free listing. The owner can unlock the full profile with a paid launch for $5:
Kimchi CodingThe governed AI coding platform
Au-Zone Technologies3D spatial perception and 4D radar-vision fusion for autonomous machines and robotics
VerdictGovernance for AI that takes action
rightcommitAI agents say done. rightcommit proves it.