How to Evaluate an AI Agent Before You Buy
James Whitfield
Head of Product
The Demo Trap
Every agent looks impressive in a demo. The creator picks a task the agent handles well, uses clean inputs, and shows a perfect output. What the demo does not show: edge cases, error states, performance under messy real-world inputs, latency at scale, and what happens when a tool it depends on is unavailable. Evaluating an agent properly means building your own test suite, not relying on the creator's cherry-picked examples. The good news: this is faster than it sounds.
Five Questions to Ask Before Deploying
First: what is the exact scope? A well-built agent has a precise definition of what it does and — critically — what it does not do. Scope creep in agent design leads to unreliable outputs. Second: what tools does it require access to, and is that access proportionate to the task? An invoice-processing agent should not need write access to your CRM. Third: how does it fail? Does it fail silently, fail loudly, or escalate to a human? Fourth: what does maintenance look like as models update? Fifth: is there an audit trail of every action the agent took?
The best agents are boring to watch in production. They just work, quietly, every time — without surprising anyone.
Building a Three-Test Evaluation
A three-test evaluation takes less than a day and catches most issues. Test one: the golden path. Give the agent a perfect, well-formed input and verify it produces the exact expected output. Test two: the edge case. Give it an ambiguous, incomplete, or malformed input and verify it fails gracefully rather than producing confidently wrong output. Test three: the adversarial case. Try to get it to do something outside its defined scope and verify it refuses cleanly. Agents that pass all three are worth deploying. Agents that fail test two or three need more work.
Evaluating Integration Depth
An agent that does not connect to your existing stack will be used by your most technically enthusiastic employee and no one else. Evaluate integration depth seriously: does it support your CRM, your ticketing system, your communication tools? Does it use OAuth for authentication or does it require storing API keys in a config file? Can it be triggered by a webhook, a scheduled cron, or a user action — or only by manual invocation? Integration depth determines whether an agent becomes embedded in your workflow or sits in a drawer.
Total Cost of Ownership
The purchase price of an agent is rarely the largest cost. Model inference costs (every tool call and reasoning step uses tokens), integration engineering time, ongoing monitoring, and the cost of fixing errors the agent makes all contribute to total cost of ownership. Ask the creator for a typical token consumption per task, multiply by your expected volume and current model pricing, and add that to the subscription cost. For high-volume workflows, inference cost often exceeds the software licence by 3x or more.
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