Decionis
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Execution pattern

Safe code deployment

Require the approval a deploy assumes it already has.

Run a live decision — no account
Category
CodeCommon in SaaS & platforms · Fintech & payments · Public sector
Works through
claude code · cursor · github
Proof
Signed Decision DossierVerifiable against our published JWKS.
Deploy
Shadow Mode firstMeasure on your own traffic before anything is stopped.

What executes

A pipeline — or an agent with repo access — ships to production, force-pushes, or runs a migration.

What normally goes wrong

The gate is a branch rule that the automation holds the credentials to satisfy. The change lands, and the review that was supposed to precede it happens during the incident.

The decision

The signals available at the moment of the action, and the rule that judges them.

Signals

  • Environment being targeted
  • Whether a human review is attached to the change
  • Blast radius of the diff (schema, secrets, infra)
  • Change-freeze windows in effect

The gate

Hold any change to a protected environment that lacks a recorded human approval, and treat schema and secret changes as always requiring one.

ALLOW

The deploy proceeds and the dossier records the approval it ran under.

ESCALATE

The deploy waits on a reviewer, with the diff and blast radius attached.

BLOCK

The deploy does not run — for example during a change freeze.

The Protocol — Deterministic. Contextual. Explainable.Four stages left to right. Input signals — user or agent identity, context and data, tools and resources, spend and budget, policies, history and reputation, and environment — enter the Decionis Protocol. Inside, the protocol normalizes context and intent, evaluates policies and rules, runs a deterministic risk and policy engine, and explains the reason and evidence. It emits a verdict: ALLOW (authorized), ESCALATE (needs approval or more info), or BLOCK (high risk or out of policy). The verdict drives an enforcement action: allow, block, require approval, or throttle and limit. Every decision is recorded for audit, learning, and continuous improvement.INPUT SIGNALSUser / Agent IdentityContext & DataTools / ResourcesSpend / BudgetPoliciesHistory & ReputationEnvironmentDECIONIS PROTOCOLNormalizeContext & IntentEvaluatePolicies & RulesRisk & Policy Engine(Deterministic)ExplainReason & EvidenceVERDICTALLOWAuthorizedESCALATENeeds approvalor more infoBLOCKHigh risk orout of policyACTIONSAllowBlockRequire ApprovalThrottle / Limit
Four stages left to right. Input signals — user or agent identity, context and data, tools and resources, spend and budget, policies, history and reputation, and environment — enter the Decionis Protocol. Inside, the protocol normalizes context and intent, evaluates policies and rules, runs a deterministic risk and policy engine, and explains the reason and evidence. It emits a verdict: ALLOW (authorized), ESCALATE (needs approval or more info), or BLOCK (high risk or out of policy). The verdict drives an enforcement action: allow, block, require approval, or throttle and limit. Every decision is recorded for audit, learning, and continuous improvement.

The policy pack that decides this pattern

The bundles the Decionis evaluator ships, with every rule in the order it is tried.

DevOps & CI

Governance for CI/CD and infrastructure workflows — Terraform apply/destroy and release/on-chain deploys.

devops · devops-v1 · 5 rules

Every policy pack the evaluator ships

Prove it, then deploy it

A verdict you cannot check is an opinion, and a gate you cannot measure first is a risk of its own.

View the signed proof

Every verdict on this pattern produces a Decision Dossier: the signals it saw, the policy version that judged them, and an Ed25519 signature anyone can check against our published JWKS — without an account, and without trusting us.

See an example dossierVerify one yourself

Deploy in Shadow Mode

Run this gate against your own traffic while it stops nothing. You get the verdicts it would have returned and the actions it would have held, so the decision to enforce is made on your numbers rather than ours.

How Shadow Mode worksTry the scenario first

Related patterns

Teams that hit this one usually hit these next.

Cloud spend increase

Commit infrastructure budget deliberately, not by autoscale.

Policy change review

Govern changes to the rules, not only actions under them.

Prevent destructive production changes

Make deleting production a decision, not a command.

Related concepts

This pattern is one answer to each of these.

Execution Control

How do I control what executes, at the moment it executes?

Policy as Code

How do I express authority as code, versioned and reviewable?

AI Approval

How do I require human approval before an AI agent acts?

Approval Evidence

What records do auditors expect for actions taken by AI agents?

What to do next

The same five steps for every pattern in the library.

  1. Simulate this recipe

    Run the policy against a realistic action in the browser. Push it past what the rules allow and watch the verdict come back. No account.

  2. View the signed Decision Dossier

    See exactly what was decided and why: the rule that fired, the evidence it read, the policy version in force, and an Ed25519 signature you can verify yourself.

  3. Deploy in shadow mode

    Measure what the policy would have caught on your own traffic without touching the live path. Every recipe defaults to shadow, so the first deployment carries no execution risk.

  4. Connect a real workflow

    Point the same policy at the system where the action actually originates — a checkout, an ERP posting, a Zap, an agent's tool call.

  5. Share the verified result

    Publish the proof: a public verification link, an embeddable badge, a PR comment, or an anonymized shadow-mode finding. This is how the next person discovers Decionis.

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