What happens when an AI agent calls an external API and the request times out?
The obvious solution seems simple: retry.
But what if the operation already succeeded?
This is one of the most interesting challenges in building reliable AI agents.
- The Problem With Blind Retries
Imagine an AI agent responsible for sending an email.
The agent sends the request to an email API.
The email is successfully delivered, but the API response is lost because of a network timeout.
The agent assumes the operation failed and retries.
Now the recipient receives the same email twice.
The problem isn't that the agent misunderstood the user's request.
The problem is that the agent couldn't determine whether the previous action had succeeded.
- Three Possible Execution States
After a tool call, an agent should distinguish between:
Success: The operation completed and the result is confirmed.
Failure: The operation definitely failed.
Unknown: The system cannot determine whether the operation completed.
The third state is especially important.
A timeout does not necessarily mean failure.
- Why Idempotency Matters
An idempotent operation can be repeated without producing additional unintended effects.
For example, an API that supports idempotency keys can recognize repeated requests and avoid creating duplicate operations.
However, not every external API supports this functionality.
Developers may also need application-level execution tracking and verification.
- A Simplified Recovery Strategy
Instead of immediately retrying every failed request, an agent could follow this process:
Execute the tool operation.
Record the execution identifier and available state.
Detect whether the result is confirmed, failed, or unknown.
For uncertain outcomes, check the external system when possible.
Retry only when the operation can be repeated safely.
Verify the final result.
If the outcome cannot be determined reliably, the system may need to report uncertainty and request user guidance.
- What We're Exploring at HEELO
At HEELO, we're building an execution layer for AI agents.
Our development and local diagnostic testing have included investigating tool execution failures and unintended repeated actions.
These scenarios demonstrate why agent reliability requires more than successful planning or basic API integration.
We're particularly interested in how execution tracking, verification, and recovery mechanisms can improve multi-step workflows.
HEELO is still under active development and testing.
- The Bigger Picture
Building an AI agent that can call tools is relatively straightforward compared with making those actions dependable.
A reliable agent needs to understand not only what it should do next, but also what may have already happened.
Retrying is a mechanism. Recovery is a strategy.
That's an important distinction for real-world AI systems.
HEELO — From Talking to Doing.
Discussion
How do you handle uncertain execution states in your AI agent systems?
Do you rely on idempotency keys, execution logs, read-after-write verification, or another recovery strategy?
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