5 Questions to Ask Before Trusting an AI Agent

You've found an AI agent that looks promising. The documentation is solid. It has good reviews. The GitHub repo is active. So you start integrating it into your workflow.

Six weeks in, it starts failing silently. You lose two days of data before someone notices. The agent is still running. It's not throwing errors. It's just returning empty results when it can't parse the input.

This happens because most agent evaluation stops before you actually understand how to trust the thing.

Trust isn't a single binary question. It's a set of specific, answerable questions about what the agent does, who built it, what happens when things break, and whether you have a fallback when they do. Get these five questions right and you can deploy with confidence. Skip them and you're gambling with production reliability.

Question 1: What Data Does This Agent Access?

This is the first thing to verify, and it's often the least documented thing about an agent. Not just "what data does the agent read" but specifically: does the agent have access to your internal databases, credentials, API keys, or customer data?

Why this matters: An agent that retrieves customer email addresses from your database and sends them to an external LLM is fundamentally different from one that processes data in an isolated sandbox. The security surface changes. The privacy compliance changes. The liability changes.

What good looks like: Clear documentation that specifies exactly which data sources the agent reads from, writes to, and passes to external services. If the agent processes customer data, there should be explicit notes on data retention, encryption, and compliance boundaries.

On OpenDraft, Verified agents document their data access patterns. Use that as your baseline.

Question 2: Who Built This and Can You Verify It?

A solo developer hobbyist and a team from an established company solving the same problem are not equivalent. The person building the agent matters — not because of ego, but because it tells you about maintenance, support, and risk.

Why this matters: If the agent breaks in production, who do you contact? How fast will they respond? Are they building this professionally or as a weekend project? An agent maintained by one person who no longer uses it is a different dependency than an agent maintained by a company whose product depends on it. You need to know which one you're getting.

What good looks like: Clear creator identity tied to a real person or organization. A visible track record, either through other projects, GitHub history, or professional background. Evidence that the creator is still actively using or maintaining the agent.

On OpenDraft, all creators go through a verification process before listing agents. You can see creator credentials directly on the listing.

Question 3: What Happens When This Agent Fails?

Every agent fails sometimes. The real question is: how does it fail? Does it throw an error, log it, and stop? Or does it silently return garbage while looking like it succeeded?

Why this matters: Silent failures are worse than loud ones. An agent that crashes hard when it hits an edge case is annoying. An agent that keeps running but returns empty results is a data loss risk. You need to know which failure mode you're getting and have a plan for it.

What good looks like: Clear documentation of known failure modes. Examples of error messages. Explanation of what conditions cause failures and how to recognize them. Ideally, monitoring or alerting integration that tells you when the agent is in a bad state.

Test the agent yourself with malformed inputs, missing data, and edge cases. Watch what happens. If it's unclear whether something worked or failed, that's a risk.

Question 4: Is There a Human Fallback?

This is the question that separates production-ready agents from interesting prototypes. If the agent fails, what happens next? Do you have a plan to handle the work manually? Is there a way to route failed requests to a human reviewer? Can you pause the agent and switch back to your old process?

Why this matters: An agent that handles 95% of requests is valuable. But what happens to the other 5%? If you don't have a fallback, those requests are lost. If your fallback is "someone manually fixes it six hours later," your effective uptime is not 95% — it's lower. The human component matters.

What good looks like: An explicit plan for what happens when the agent fails. A queue or log of unhandled requests that a human can review. A kill switch to pause the agent without bringing down your whole system. Ideally, a gradually escalating fallback — the agent handles what it can, flags uncertain cases for human review, and routes only obvious failures to a backup process.

Question 5: How Is Performance Actually Measured?

Most agents don't ship with real performance data. What you get instead is a demo, a test case, and assumptions. "Our benchmarks show 94% accuracy" tells you almost nothing without knowing what "accuracy" means, what the test set looks like, whether it included edge cases, and whether it matches your actual use case.

Why this matters: An agent that performs well in a lab setting can fail in production because your data is messier, your edge cases are different, or your volume is higher. You need to know how the agent actually behaves with your specific inputs, your scale, and your failure patterns — not just the creator's test cases.

What good looks like: Published benchmarks with clear descriptions of the test set and methodology. Real-world performance data from users who've deployed the agent in production. Clear documentation of token usage, API costs, and latency ranges. An invitation to test the agent yourself with your own data before committing to it.

On OpenDraft, real users rate agents after deploying them. You can see what actual performance looked like, not just what the creator claims.

Putting It Together: The Trust Framework

These five questions form a trust framework. You don't need a perfect score on all of them — you need to understand the risks and have a mitigation plan for each one.

If an agent has poor answers to most of these five questions, don't deploy it without adding significant validation work on your end. If it has clear answers to all five, you can move to production faster.

The agents worth deploying are the ones where the creator has already answered these questions for you.