Tech leads and engineering managers are responsible for shipping velocity and system reliability simultaneously. When team members adopt AI agents, the tech lead needs a safety layer that does not require weeks of integration, does not slow down individual developers, and provides visibility into what agents are doing. SafeClaw by Authensor is an open-source tool that installs in 60 seconds with npx @authensor/safeclaw, enforces deny-by-default action gating through a single YAML policy, and logs every agent action in a hash-chained audit trail.

The Tech Lead's Challenge

You cannot ban AI agents — your team is already using them and getting real productivity gains. But you also cannot allow unrestricted agent access to your codebase, infrastructure, and credentials. The tech lead's challenge is finding the middle ground:

• Standardize safety across the team — every developer's agent should follow the same baseline rules
• Avoid blocking productive work — the safety layer should be invisible for safe operations and only intervene for risky ones
• Maintain visibility — know what agents are doing across your team without requiring manual reporting
• Integrate with existing workflow — safety should be a config file, not a new service to maintain

Team-Wide SafeClaw Policy

Create a safeclaw.yaml in your project root and commit it to version control. Every developer on the team gets the same policy:

# safeclaw.yaml — engineering team baseline
version: 1
default: deny

rules:
  # Code access
  - action: file_read
    path: "src/**"
    decision: allow
    reason: "Source code is readable"

- action: file_read
    path: "tests/**"
    decision: allow
    reason: "Test files are readable"

- action: file_write
    path: "src/**"
    decision: prompt
    reason: "Code writes need developer review"

- action: file_write
    path: "tests/**"
    decision: allow
    reason: "Test generation is safe"

# Safety boundaries
  - action: file_read
    path: "*/.env"
    decision: deny
    reason: "No access to environment files"

- action: file_write
    path: ".config."
    decision: deny
    reason: "Config files are protected"

- action: shell_execute
    command: "npm test"
    decision: allow
    reason: "Running tests is safe"

- action: shell_execute
    command: "npm run lint"
    decision: allow
    reason: "Linting is safe"

- action: shell_execute
    command: "npm install *"
    decision: prompt
    reason: "Package installs need review"

- action: shell_execute
    command: "rm *"
    decision: deny
    reason: "Block file deletion"

- action: shell_execute
    command: "git push*"
    decision: prompt
    reason: "Pushes need developer approval"

This policy allows agents to freely read code and generate tests, but gates code writes behind a human review prompt. Destructive operations like file deletion are denied. Package installations require approval to prevent supply chain attacks.

Rolling Out to Your Team

The rollout process for a tech lead:

1. Start in simulation mode. Run npx @authensor/safeclaw --simulate for one sprint. Review the logs to see what agents are doing and what the policy would have blocked.
2. Refine the policy. Adjust rules based on actual usage patterns. If agents frequently need to write to docs/, add an allow rule for that path.
3. Switch to enforcement. Remove the --simulate flag. The policy is now active.
4. Add to CI. If your team uses AI agents in CI/CD pipelines, add SafeClaw enforcement as a pipeline step.
5. Review audit logs. Periodically check the hash-chained audit trail to understand agent behavior trends.

Why SafeClaw Works for Team Leads

SafeClaw is MIT-licensed and open source — no procurement process, no per-seat licensing, no vendor dependency. It has zero external dependencies, so it does not add security risk to your supply chain. The 446-test suite means the policy engine behaves predictably. It supports both Claude and OpenAI, so your team's choice of AI provider is irrelevant. The entire configuration is a single YAML file that lives in version control and goes through code review like everything else.

Related pages:

• Team AI Agent Policy Rollout Guide
• Simulation Mode Reference
• Policy Design Principles
• Code Review AI Safety Workflow