Decionis
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Execution pattern

Prevent AI overspending

Cap what an agent can spend before the spend happens, not in next month's bill.

Run a live decision — no account
Category
MoneyCommon in SaaS & platforms · Fintech & payments · Commerce & retail
Works through
aws · copilot · langchain · sdk +2 more
Proof
Signed Decision DossierVerifiable against our published JWKS.
Deploy
Shadow Mode firstMeasure on your own traffic before anything is stopped.

What executes

An agent calls a paid API, provisions a resource, or loops on a task that bills per token.

What normally goes wrong

Nobody notices until the invoice arrives. Agent cost is discovered retrospectively because the spend decision was never a decision — it was a side effect of a retry loop.

The decision

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

Signals

  • Cumulative spend in the current window
  • Cost of the specific call being attempted
  • Budget delegated to this actor
  • Whether the request is a retry of work already paid for

The gate

Refuse the call when it would take the actor past the budget delegated to it, and escalate when it crosses a threshold someone agreed to review.

ALLOW

The call proceeds and the spend is recorded against the actor's budget.

ESCALATE

The call waits for whoever owns the budget to approve the overage.

BLOCK

The call does not run; the dossier records the budget rule that stopped it.

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.

No policy pack decides this pattern yet

Write the gate above as your own policy: a repository policy file, or a bundle in your workspace.

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.

Gate agent memory and retrieval

Decide what an agent may read, and what it may keep.

Cloud spend increase

Commit infrastructure budget deliberately, not by autoscale.

Control MCP egress

Decide what leaves before it leaves, at the tool-call boundary.

Related concepts

This pattern is one answer to each of these.

AI Spending

How do I control what an AI agent spends?

Agent Permissions

How do I decide what an AI agent is permitted to do?

Policy Engine

What is a policy engine, and where does it belong?

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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