Avoid these mistakes when building AI agents with external APIs
Don't make these four mistakes and ship more robust AI integrations
AI agents are unlocking new ways to automate workflows, but integrating them with real-world third party APIs is where things often fall apart.
This is because building AI-powered integrations is fundamentally different from traditional API development. Agents don’t just call endpoints. They infer intent, synthesize inputs, and try to stitch together multi-step actions. This introduces a whole new set of failure modes.
We’ve seen teams hit the same pitfalls over and over again. Here are the top 4 mistakes, and how to void them.
Unclear or missing schemas
The biggest mistake? Letting the agent “figure out” what the API expects.
When the input/output schemas aren’t clearly defined, agents send:
- Parameters with the wrong names
- Data in the wrong format
- Partial or ambiguous requests
Third party APIs come with strict parameters. They need precision. Without a clear schema, preferably in a structured format like OpenAPI or JSON Schema, you’re just hoping the agent guesses right while wasting money.
What’s the fix?
Define strong, typed schemas and validate inputs before execution. Treat every API call like a contract.
Assuming agents can chain API calls reliably
A human can reason through a 3-step workflow. Agents? Not so much.
Expecting an LLM to:
- Search a contact in HubSpot,
- Get the ID,
- Then update their lifecycle stage…
...is fragile at best.
If one step fails, the chain breaks. If the agent forgets a field or misinterprets a response, the whole thing derails.
What’s the fix?
If multiple API requests are needed to accomplish a task, execute them deterministically with code. Then expose the function to the agent, so it only needs to get one input right.
Not providing enough context
LLMs are only as good as the context you give them.
If your agent needs to update a record, but doesn’t know what records exist or what fields are allowed, it’ll hallucinate.
Many teams forget to ground the agent in real, current data from the API.
What’s the fix?
Use lightweight data syncs or RAG (retrieval-augmented generation) to give the agent the context it needs. Sync the necessary and most relevant context, not the whole database.
Overlooking rate limits
Search endpoints (e.g., “find this user in CRM”) are tempting to hit in real-time, especially when the agent is responding dynamically.
But the reality is that it doesn’t scale. Every request adds latency, and most third party APIs have tight rate limits and their own quirks.
The result? Slow performance, inconsistent behavior, or 429 errors (too many requests).
What’s the fix?
Treat external APIs as a scarce resource: set up continuous data syncs to pre-fetch and cache frequently needed data (like contacts or company info), and reserve real-time API calls for infrequent lookups. This drastically improves performance and reliability.
Conclusion
AI agents won’t magically “just work” with all third party APIs. You need to design the surrounding infrastructure thoughtfully, just like you would for any critical system.
Avoiding the above four mistakes will help you ship robust, production grade AI agents much faster.
