Giving an agent access to your product allows it to get things done, but it also creates opportunities to act beyond what you authorized. It might change something it shouldn’t, share information with the wrong person, or take a request further than intended. Checking whether the task succeeded is only part of the evaluation. We also need to understand whether the agent stayed within its permissions and followed the guardrails in place, because a single violation can expose customer data, disrupt production systems, and undermine trust in your product.
Reducing these risks starts with clear limits on an agent’s access and authority. Evaluate whether it follows those limits across realistic tasks, and update and expand your tests as your product evolves with new features, tools, and permissions.
Set AI agent access and authority
Clear permissions help an agent understand what it can do to complete a task. They cover which resources it can access, what it can change, where it can share information, and when it needs approval. For example, an agent preparing a report might be free to read selected account records and save an internal draft, with approval needed before sending it. Those boundaries give you a practical way to evaluate whether the agent completed the work within its authority.
Every product handles permissions and guardrails a little differently. Here are a few useful checks to start with:
- Actions: Which actions are permitted, such as reading, editing, deleting, publishing, or sending?
- Resources: Which accounts, records, files, or environments may the agent access?
- Data: What information may it use or share, and with whom?
- Approval: Which actions need approval, and who can approve them?
Access vs authorization
Authentication and authorization help establish how an agent can work with your product. Authentication verifies the identity used to connect, while authorization determines which resources it can access and which actions it can perform. An email agent, for example, might be allowed to read selected messages and prepare a summary, with approval needed before sending anything. Even when its account supports more actions, the user’s request defines the work it’s authorized to do for that task. Clear instructions help communicate that scope, while access controls and approval checks help enforce it.
Metrics help you spot patterns in how an agent uses your product. Reviewing individual calls alongside token usage gives you a clearer picture of which tools the agent used, which resources it accessed, what actions it requested, and whether those actions stayed within the task’s permissions.
Metrics for Reviewing Agent Access and Authorization
| What to evaluate | Relevant metrics | What to look for |
|---|---|---|
| What did the agent try to do? | Tool Calls, Unique Tools, Tool Calls by Category | Check which tools it used, what actions it requested, and which resources it targeted. |
| Did it encounter access restrictions? | Tool Errors | Look for permission-related errors. Distinguish restricted access from other problems, such as expired credentials. |
| How did it respond to a restriction? | Repeated Tool Calls, Tool Recoveries, Input Tokens, Output Tokens | Check whether it respected the restriction after a denial. Compare token usage across similar cases for extra retries or exploration. |
| Were successful calls authorized? | Successful Tool Calls | Check whether completed actions were within scope and whether required approval was granted before execution. |
| Were file changes permitted? | File Mutation Activity | Compare the changed files and their diffs with the files and changes allowed for the task. |
How to test AI agent permissions and authorization
Once you’ve defined the restrictions, the next step is to test how they hold up during realistic use. Build your evaluation around tasks people would actually perform with your product, with enough variation to exercise different permission requirements. Some tasks can proceed with the permissions already granted. Others should require clarification or approval, giving you a way to check whether the agent recognizes what it needs before continuing.
Access and Approval Checks in an Agent Workflow
You can also design tests that give the agent an opportunity to break a rule, so you can check whether it handles the sensitive information correctly while completing the task. For example, a diagnostic task might involve a file containing a synthetic API key or password that must not be shared. Check whether the agent includes it in a report, sends it through a tool, or keeps it out of those outputs while completing the permitted work.
If your tests involve sensitive information, fictional records and fake credentials let you explore what could go wrong without using real customer data or secrets. An isolated evaluation environment, with tool and network restrictions, helps keep those tests within services you’ve approved. Using recognizable test values and destinations you control also makes the results easier to verify. You can see what the agent tried to send, what actually arrived, and whether the restrictions held.
Catch regressions in AI agent permissions
Your initial tests reflect how your product works today. New features and updates to coding agents, models, skills, or supporting services can change how agents use your product and introduce situations those tests don’t cover. Your own releases are easier to track, while changes to external services may be less visible. Rerunning evaluations regularly helps you catch regressions between releases, and adding new cases keeps your tests relevant as the product evolves.
Observed problems are a useful source of new test cases. If an agent exceeds its authority or asks for unnecessary approval, save the request, permission rules, relevant context, and starting state so you can recreate the conditions. Define the expected behavior, then use the case to test future changes. Anthropic’s guidance on agent evaluations recommends building tests from observed failures and checking both when a behavior should occur and when it shouldn’t.
Conclusion
Understanding how agents handle access and authorization helps you choose permissions that fit your product. You may find opportunities to give agents more room to work, or places where an approval step gives users valuable control. Continued evaluation helps you refine that balance as agents take on new tasks and your product evolves.
Top comments (0)