Where AI Agent Tests Go Wrong: From Tool Calls to Real-World Verification

Building an AI agent that can call external tools is one challenge.

Building an agent that can reliably complete real-world tasks is another.

A test might pass because a function executed or a failure was correctly reproduced. But that doesn't necessarily mean the user's intended task was completed.

Let's examine where AI agent workflows can fail.

A typical tool-using agent might follow this sequence:

User Intent → Planner → Tool/API → Verifier → Observed Outcome

Each stage introduces potential failure points.

The agent may attempt to execute an operation without providing all the required arguments.

For example, a calendar tool might expect both a start time and an end time.

Missing one of these values can cause an exception or prevent the operation from completing.

An API request might time out even after the external service has processed it.

The agent must distinguish between a failed request and an uncertain execution state.

A tool might return a response that appears successful even though the intended real-world change didn't occur.

A response status alone may not be sufficient evidence of task completion.

Retrying an operation without checking previous execution results can produce unintended repeated actions.

This is especially important for tools that send emails, create events, or modify external data.

Consider a diagnostic test written to reproduce a known failure.

If the expected exception occurs, the diagnostic test may be marked as passed.

However, the underlying application behavior remains defective.

This creates two different definitions of success:

Diagnostic success: The test successfully reproduced or detected the expected behavior.

User-task success: The agent actually completed the user's request correctly.

These results should be reported separately.

At HEELO, we've investigated tool-execution reliability through local diagnostic tests.

Our testing has included scenarios involving missing calendar parameters and unintended repeated email actions.

These diagnostic cases illustrate why execution evidence matters.

The objective is to distinguish successful defect detection from successful product execution.

Reliable agent evaluation should ask more than whether an API returned successfully.

Outcome verification: Can the resulting state be checked?

Execution correctness: Were the appropriate tools and parameters used?

Recovery behavior: Can the system recover safely from timeouts and partial failures?

Traceability: Can developers reconstruct what actually happened?

For state-changing operations, idempotency and confirmation mechanisms can also reduce unintended duplicate actions.

At HEELO, we're developing an execution layer for AI agents.

Our focus includes tool integration, multi-step workflows, and the testing required to make agent execution more reliable.

The system is still under development, and evaluating failure scenarios is part of that work.

Ultimately, agent reliability should be measured not only by whether an operation ran, but by whether it achieved the correct and verifiable outcome.

For developers building tool-using AI agents:

What's the most difficult part of ensuring reliable execution?

I'd be interested to hear how others approach these problems.