How to Set AI Agent Policies for Engineering Teams

Setting AI agent policies for engineering teams requires a centralized, version-controlled policy engine that enforces consistent rules across every developer's workstation. SafeClaw by Authensor provides exactly this: a deny-by-default policy engine where teams define allowed actions in YAML, commit them to version control, and every AI agent action is gated before execution. No agent can read, write, execute, or make network calls unless the policy explicitly permits it.

Quick Start

Install SafeClaw on every developer machine:

npx @authensor/safeclaw

Create a shared team policy file at .safeclaw/team-policy.yaml in your repository root:

version: "1.0"
description: "Engineering team baseline policy"

rules:
  - action: file.read
    path: "src/**"
    effect: allow
    reason: "Read access to source code"

- action: file.write
    path: "src/**"
    effect: allow
    reason: "Write access to source code"

- action: file.read
    path: ".env*"
    effect: deny
    reason: "Block access to environment secrets"

- action: shell.execute
    command: "rm -rf *"
    effect: deny
    reason: "Prevent destructive deletions"

- action: network.request
    domain: "*.internal.company.com"
    effect: allow
    reason: "Allow internal API access"

- action: "*"
    effect: deny
    reason: "Deny everything not explicitly allowed"

Structuring Team-Wide Policies

Organize policies in a hierarchy that maps to your team structure:

.safeclaw/
  base-policy.yaml          # Organization-wide baseline
  team-backend.yaml         # Backend team overrides
  team-frontend.yaml        # Frontend team overrides
  team-data.yaml            # Data engineering overrides
  onboarding-restricted.yaml # New developer constraints

SafeClaw evaluates rules using first-match-wins semantics. Place more specific rules above general ones. The final catch-all deny rule ensures nothing slips through.

Onboarding New Developers

New team members should start with a restricted policy that gradually expands:

version: "1.0"
description: "Onboarding policy — first 30 days"

rules:
  - action: file.read
    path: "src/**"
    effect: allow

- action: file.write
    path: "src/tests/**"
    effect: allow
    reason: "New devs can write tests only"

- action: shell.execute
    command: "npm test"
    effect: allow

- action: shell.execute
    command: "npm run lint"
    effect: allow

- action: "*"
    effect: deny
    reason: "Restricted during onboarding"

After the onboarding period, promote the developer to the standard team policy by updating their local SafeClaw configuration or switching the policy file reference.

Enforcing Policy Consistency

Commit .safeclaw/ to your repository. Add a CI check that validates policy syntax:

npx @authensor/safeclaw validate --policy .safeclaw/base-policy.yaml

This ensures no broken policies reach production. SafeClaw's 446 built-in tests cover edge cases in policy evaluation, so you can trust the engine behaves predictably.

Monitoring Policy Violations

Every denied action is logged to SafeClaw's hash-chained audit trail. Review team-wide violations:

npx @authensor/safeclaw audit --filter effect=deny --since "7 days"

Use these logs in weekly security reviews to identify overly restrictive rules (causing friction) or suspicious patterns (indicating misuse).

Why SafeClaw

• 446 tests ensure policy evaluation correctness across every edge case
• Deny-by-default means agents have zero permissions until explicitly granted
• Sub-millisecond evaluation adds no perceptible latency to agent workflows
• Hash-chained audit trail provides tamper-proof logging for every action decision
• Works with Claude AND OpenAI — provider-agnostic by design
• MIT licensed — free to use, modify, and distribute in any environment

See Also

• Role-Based Access Control for AI Agents
• AI Agent Compliance Reporting
• Building an AI Governance Framework with SafeClaw
• Policy-as-Code Pattern