Responsible AI Agent Development - Building Agents That Do No Harm
Building AI Agents That Do No Harm
An AI agent with access to your operating system can do a lot of good. It can also delete your files, send emails you didn't authorize, or modify system settings in ways that break your workflow. The difference comes down to how you build the guardrails.
Scope Limiting Is the First Defense
Every agent should have a clearly defined scope of what it can and cannot do. A file organization agent should not have permission to send emails. A code review agent should not be able to push to production.
In practice, this means:
- Whitelisting actions instead of blacklisting. Define what the agent can do, not what it can't
- Read-only by default. Write access should be explicitly granted per task
- No cascading permissions. An agent that can install packages shouldn't automatically get the ability to run arbitrary shell commands
Output Validation Before Execution
Never let an agent execute actions without a validation step. This doesn't mean asking the user to approve every click - that defeats the purpose of automation. Instead, validate outputs programmatically:
- File modifications should pass a diff check against expected patterns
- Commands should be compared against an allowlist before execution
- Network requests should be limited to known domains
The validation layer sits between the agent's decision and the actual system call. It's the seatbelt that catches mistakes before they happen.
Reversibility Matters
Design agents so their actions can be undone. Before modifying a file, create a backup. Before changing a system setting, record the previous value. Before sending a message, show a preview with a cancel window.
Irreversible actions - deleting data, sending communications, modifying shared resources - should always require explicit human confirmation regardless of the agent's confidence level.
The Compound Error Problem
A single bad decision by an agent is usually recoverable. The danger is when agents chain actions together and errors compound. An agent that misreads a file, then modifies another file based on that misreading, then triggers a build based on the modification - each step amplifies the original error.
Break chains with checkpoints. Validate intermediate results before proceeding to the next step.
Fazm is an open source macOS AI agent. Open source on GitHub.