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Your API is Broken for Agents: A Guide to Testing Function Calling in 2026

January 18, 2026 • PrevHQ Team

You have 100% code coverage. Your OpenAPI spec is valid. Your Postman collection is a work of art.

But when you plug your API into a generic LLM (like GPT-6 or Claude 4.5) and give it a task, it fails.

It hallucinates a parameter. It tries to call GET /users with a POST body. It gets a 400 Bad Request, apologizes, and then does the exact same thing again.

Your API works perfectly for humans. It is completely broken for agents.

In 2026, this is a business-critical failure. If an agent can’t use your tool, it can’t buy your product.

The Human vs. Agent Gap

We spent the last 15 years designing APIs for “The Developer Experience” (DX).

  • Intuitive: “Guessable” URLs.
  • Forgiving: Loose validation.
  • Documentation: Beautiful static sites with examples.

Agents don’t care about your documentation site. They don’t have “intuition.” They care about one thing: The Context Window.

When an agent uses your API via Function Calling (or the Model Context Protocol), it is looking at a compressed, tokenized version of your schema. If your parameter description is vague, the agent guesses. And because LLMs are probabilistic, it guesses differently every time.

The “Retry Loop” of Death

The most dangerous pattern in Agentic Engineering is the Retry Loop.

  1. Agent calls API.
  2. API returns 400 Bad Request: Invalid Input.
  3. Agent reads error, thinks “Oh, I should try a different format.”
  4. Agent calls API again (with a new hallucination).
  5. Repeat until your token budget is drained or the rate limiter bans you.

For a human developer, a 400 error is a learning moment. For an AI agent, a 400 error is a challenge to be “creative.” You do not want creative API clients.

You Can’t Unit Test Probability

So how do you fix this? You can’t fix it with standard integration tests.

A standard test checks: “If I send X, do I get Y?” An Agentic test must check: “If I give the Agent goal G, does it figure out it needs to send X?”

This is a fundamental shift. You are no longer testing the endpoint. You are testing the affordance.

The Agentic Sandbox

This is why leading API teams are moving to Agentic Integration Testing. They aren’t just running npm test. They are running npm run test:agent.

This is the primary use case for PrevHQ in 2026.

  1. Spin Up: For every PR, PrevHQ creates an ephemeral sandbox of your API.
  2. The Prompt: We point an un-tuned, generic LLM at the sandbox with a high-level goal: “Register a new user and upgrade them to Pro.”
  3. The Watcher: We monitor the execution trace.
    • Did the agent pick the right tool?
    • Did it hallucinate a subscription_id?
    • Did it get stuck in a loop?

Optimizing for the Machine

If the agent fails, you don’t fix the agent (you can’t fix OpenAI). You fix your API.

  • You rename create_sub to create_subscription_v2.
  • You add a description field to the schema: “Use this ONLY after creating a user.”
  • You change the 400 error message from “Invalid Input” to “Missing field: user_id. Please retrieve user_id first.”

You are optimising your API for a non-human intelligence.

The Interface is the Product

In the Agent Economy, your API is your only UI. If the machine can’t figure it out in 3 seconds (or 3 tokens), it moves on to your competitor.

Don’t assume your API is ready just because it works in curl. Test it against the intelligence that will actually be using it.

FAQ: Testing APIs for AI Agent Function Calling

Q: What is “Function Calling” in LLMs?

A: The ability of an AI to execute code. Instead of just generating text, the LLM generates a structured JSON object representing an API call (e.g., {"tool": "get_weather", "params": {"city": "London"}}). Testing this requires verifying that the model consistently generates valid JSON for your specific schema.

Q: Why do agents hallucinate API parameters?

A: Ambiguous Schema. If your OpenAPI spec says id (string) but doesn’t specify which ID (user ID? account ID?), the LLM guesses based on probability. You must provide verbose, deterministic descriptions in your schema (e.g., “The UUID of the user returned by /create-user”).

Q: How does Model Context Protocol (MCP) change this?

A: Standardization. MCP provides a standard way for agents to “discover” your API’s capabilities. However, it doesn’t solve the ambiguity problem. You still need to test that your MCP server provides clear enough context for the agent to succeed.

Q: Can I use PrevHQ to debug agent failures?

A: Yes. PrevHQ provides a full network trace of the agent’s interaction with your sandbox. You can see exactly what the agent sent, what your API returned, and how the agent “reasoned” about the error in the next step.

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