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Reliability & production

Published Jul 31, 2026·Updated Aug 30, 2026

![](https://cohesivity.ai/authors/anshu.webp)![](https://cohesivity.ai/authors/arag.webp)[Anshu](https://www.linkedin.com/in/aanshuaggrawal120/) and [Arag](https://www.linkedin.com/in/aragagrawal/)

# AI Agent Governance: Policies for Autonomous Software

[Read as Markdown](https://cohesivity.ai/blog/ai-agent-governance-policies-for-autonomous-software.md)

Governance

Agent

Limits

Policy

Oversight

AI agent governance defines which identities may take which actions, under what conditions, with what approval, limits, evidence, and recovery. A useful policy can be translated into an enforceable rule and a test; a sentence such as “agents must act responsibly” cannot.

Write policy around concrete actions and consequences, then connect it to permissions, approval gates, spend controls, telemetry, evaluations, and incident response.

OWASP's [Excessive Agency guidance](https://genai.owasp.org/llmrisk/llm062025-excessive-agency/) calls for minimizing tool functionality, permissions, and autonomy while enforcing authorization downstream. Governance decides the permitted boundary; engineering makes it real.

## Start with an inventory of capabilities

List every deployed agent, owner, environment, represented users, visible tools, data sources, credentials, external destinations, model and prompt versions, and maximum autonomous action. Include background agents and internal tools that lack a chat interface.

For each action, record:

- subject and tenant;
- resource and operation;
- conditions and numerical limits;
- whether approval is required and who may approve;
- enforcement point;
- audit evidence and retention;
- exception owner and expiry;
- failure, cancellation, and recovery behavior.

This becomes the map between policy language and the [agent control plane](https://cohesivity.ai/blog/how-to-design-an-agent-control-plane).

## Classify actions by consequence

| Class                     | Example                                           | Default control                                                       |
| ------------------------- | ------------------------------------------------- | --------------------------------------------------------------------- |
| Read                      | Retrieve an owned order                           | Scoped identity, data minimization, audit metadata                    |
| Reversible write          | Update a draft record                             | Authorization, idempotency, bounded autonomy                          |
| External communication    | Send an email or publish content                  | Recipient and content constraints, approval where needed              |
| Financial action          | Purchase, refund, or provision paid capacity      | Amount and period limits, approval, reconciliation                    |
| Destructive action        | Delete data or revoke access                      | Narrow permission, explicit approval, reversible path where supported |
| Privileged administration | Change policy, credentials, billing, or ownership | Separate identity, strong approval, detailed audit                    |

The table is a starting point, not a universal risk score. A read can expose sensitive data, while a reversible draft update may have little consequence. Add data classification, tenant commitments, and workflow context.

## Express policy as a decision contract

A concrete rule might read:

> A support agent may issue one refund up to the documented threshold for an order owned by its tenant when the authenticated user has the support role. Larger or repeated refunds require finance approval. Every attempt uses an operation ID and records the policy version and outcome.

Engineering can implement and test that. Operations can investigate it. The business owner can challenge the threshold. Keep thresholds and policy versions in controlled configuration rather than scattering them through prompts.

Prompts may explain the rule to the model, but the backend must enforce resource ownership, amount, frequency, role, and approval.

## Govern data and model inputs

Define which data classes an agent may retrieve, send to each model provider, retain in memory, place in traces, and expose to tools. Record the provenance of retrieved content and treat it as untrusted input.

Specify deletion, correction, and retention paths across prompts, memories, artifacts, evaluation fixtures, traces, and backups. Limit production data in offline evaluations and require sanitization for incident-derived cases.

The [agent security guide](https://cohesivity.ai/blog/ai-agent-security-identity-permissions-secrets-audit-logs) covers credential and downstream authorization controls.

## Version models, prompts, tools, and policies

An agent’s behavior can change when any of these surfaces changes. Record their versions on each run and require the relevant evaluation suite before promotion. Keep a rollback path for prompts and tool contracts, not only application code.

Changes that expand permissions, external destinations, financial limits, or autonomous action need an explicit owner and review. A new model with the same prompt may still change tool choices, so treat it as a governed behavior change.

## Make evidence queryable

Governance evidence should connect the policy decision, approval, tool call, backend operation, and result. Store stable identifiers and redacted facts. Do not collect full prompts or personal data merely to make an audit record look complete.

Define who can access evidence and how long it is retained. An audit system with broad cross-tenant data creates its own privileged surface.

## Time-box exceptions

An exception should name its owner, affected agents, reason, compensating control, start, expiry, and review condition. Expire it automatically when possible. “Temporary” access without a date tends to become the new policy without review.

Track repeated exceptions as evidence that the base policy or product workflow needs revision.

## Connect policy to incidents and evaluations

The incident path should stop new runs, revoke credentials, identify affected operations, preserve evidence, reconcile outcomes, recover data, notify required parties, and produce a regression case.

The [production agent checklist](https://cohesivity.ai/blog/the-production-ai-agent-checklist) turns those controls into a workflow-level review. Every meaningful incident should sharpen a policy, enforcement point, alert, runbook, or evaluation.

## Test one sentence end to end

Choose a policy with a number, action, or approval. Attempt an allowed case, a boundary case, an unauthorized tenant, an expired approval, and a repeated operation in an isolated environment. Confirm the backend decision and audit evidence match the written rule.

The prompt below performs that translation against the current repository. If it cannot identify an enforcement point and acceptance test, the policy is still guidance rather than governance.

or send it to[Claude Code](https://claude.ai/new?q=Inspect+this+repository%27s+AI+agent+policies%2C+prompts%2C+permission+checks%2C+approval+gates%2C+limits%2C+logs%2C+and+runbooks.+Do+not+edit+code+or+call+external+services.+Choose+one+high-impact+policy+sentence+and+translate+it+into+subject%2C+tenant%2C+resource%2C+action%2C+conditions%2C+limits%2C+approval%2C+enforcement+point%2C+evidence%2C+exception+owner%2C+and+failure+behavior.+Compare+the+written+rule+with+the+current+implementation.+List+ambiguous+terms+and+controls+enforced+only+by+prompts%2C+then+propose+testable+acceptance+criteria+and+the+smallest+implementation+gap. "Send to Claude")[Cursor](https://cursor.com/link/prompt?text=Inspect+this+repository%27s+AI+agent+policies%2C+prompts%2C+permission+checks%2C+approval+gates%2C+limits%2C+logs%2C+and+runbooks.+Do+not+edit+code+or+call+external+services.+Choose+one+high-impact+policy+sentence+and+translate+it+into+subject%2C+tenant%2C+resource%2C+action%2C+conditions%2C+limits%2C+approval%2C+enforcement+point%2C+evidence%2C+exception+owner%2C+and+failure+behavior.+Compare+the+written+rule+with+the+current+implementation.+List+ambiguous+terms+and+controls+enforced+only+by+prompts%2C+then+propose+testable+acceptance+criteria+and+the+smallest+implementation+gap. "Send to Cursor")[Codex](https://chatgpt.com/codex?prompt=Inspect+this+repository%27s+AI+agent+policies%2C+prompts%2C+permission+checks%2C+approval+gates%2C+limits%2C+logs%2C+and+runbooks.+Do+not+edit+code+or+call+external+services.+Choose+one+high-impact+policy+sentence+and+translate+it+into+subject%2C+tenant%2C+resource%2C+action%2C+conditions%2C+limits%2C+approval%2C+enforcement+point%2C+evidence%2C+exception+owner%2C+and+failure+behavior.+Compare+the+written+rule+with+the+current+implementation.+List+ambiguous+terms+and+controls+enforced+only+by+prompts%2C+then+propose+testable+acceptance+criteria+and+the+smallest+implementation+gap. "Send to Codex")[opencode](https://opencode.ai/?q=Inspect+this+repository%27s+AI+agent+policies%2C+prompts%2C+permission+checks%2C+approval+gates%2C+limits%2C+logs%2C+and+runbooks.+Do+not+edit+code+or+call+external+services.+Choose+one+high-impact+policy+sentence+and+translate+it+into+subject%2C+tenant%2C+resource%2C+action%2C+conditions%2C+limits%2C+approval%2C+enforcement+point%2C+evidence%2C+exception+owner%2C+and+failure+behavior.+Compare+the+written+rule+with+the+current+implementation.+List+ambiguous+terms+and+controls+enforced+only+by+prompts%2C+then+propose+testable+acceptance+criteria+and+the+smallest+implementation+gap. "Send to OpenCode")