Guardrails & Safety for Autonomous Agents

Agents will make mistakes. Design assuming they will:

Key principles:

Bounded autonomy: Agents should have clearly defined limits on what they can do. More autonomy = more capability but more risk.

Defense in depth: Multiple layers of protection. If one fails, others catch it.

Fail safe, not fail deadly: When something goes wrong, default to safe behavior (stop and ask) not dangerous behavior (continue and hope).

Reversibility: Prefer reversible actions. When irreversible actions are needed, require extra verification.

Transparency: Be able to explain every action the agent took and why. No black boxes in production.

Progressive trust: Start with tight constraints. Loosen as you build confidence. Not the reverse.

Protect against malicious or problematic inputs:

Prompt injection defense: Users may try to manipulate the agent through crafted inputs. - Clearly separate user input from instructions - Validate inputs before including in prompts - Use structured formats rather than raw text injection - Monitor for injection patterns

Input validation: - Check format and content of user inputs - Reject clearly invalid requests - Sanitize before passing to agent - Log suspicious inputs for review

Scope enforcement: - Define what topics/tasks are in scope - Reject out-of-scope requests early - Don't rely on prompt instructions alone

Rate limiting: - Limit requests per user/session - Prevent abuse and runaway costs - Slow down potential attacks

Constrain what agents can actually do:

Permission systems: Define explicit permissions for each action: - READ: Can retrieve information - WRITE: Can modify data - DELETE: Can remove data - EXECUTE: Can trigger external actions

Different tasks/users get different permissions.

Action validation: Before executing any action: - Is this action permitted? - Are parameters valid? - Is this consistent with the task? - Would a reasonable human do this?

Approval requirements: High-risk actions require approval: - Monetary transactions - Sending external communications - Deleting data - Accessing sensitive information

Sandboxing: Dangerous operations (code execution, file system) run in sandboxed environments with limited permissions.

Validate what the agent produces before it reaches users or systems:

Content filtering: - Check for harmful/inappropriate content - Verify factual claims where possible - Ensure tone matches requirements - Catch confidential information leaks

Format validation: - Does output match expected structure? - Are required fields present? - Do values fall in expected ranges?

Consistency checks: - Does output contradict known facts? - Is it consistent with earlier outputs? - Does it make logical sense?

Human review triggers: Automatically flag for human review: - Low confidence scores - Unusual patterns - First occurrence of new output types - Random sample for quality assurance

Fallback responses: When output fails validation: - Don't show invalid output to users - Provide graceful fallback message - Log for investigation - Escalate if repeated failures

Safety at the system level:

Monitoring and alerting: - Track success/failure rates - Alert on anomalous behavior - Monitor resource usage - Watch for cost explosions

Circuit breakers: - Automatically pause if error rate spikes - Stop specific workflows if they're failing - Kill switch for emergency shutdown

Audit logging: Every action the agent takes must be logged: - What action - What inputs - What outputs - Who requested - When it happened - Full reasoning trace

Recovery procedures: - How to roll back agent actions - How to restart from checkpoint - How to recover corrupted state - How to handle partial failures

Testing in production: - Shadow mode (agent suggests, humans act) - Gradual rollout (small % of traffic) - A/B testing (agent vs. human) - Continuous evaluation on real data