The biggest mistake I see in building AI agents isn't the model—it’s the way we handle tool-calling loops.

The biggest mistake I see in building AI agents isn't the model—it’s the way we handle tool-calling loops.

Most devs implement a naive while-loop that just feeds tool outputs back to the LLM. The problem? Context window bloat and "hallucination drift" where the agent gets lost in its own previous function calls.

To fix this, stop treating the conversation history as a monolithic log. Start using a state-management pattern that separates tool results from the core reasoning trace.

When your agent calls a tool, don't just dump the raw JSON output into the prompt. Create a summary layer or a dedicated result-processor that extracts only the signal relevant to the next step.

For example, if your agent is querying a SQL database, have a mini-script that parses the recordset into a succinct natural language summary before injecting it back into the context. This keeps your token count stable and significantly reduces the probability of the model misinterpreting noisy data.

Efficient agents aren't just faster—they’re more predictable.

What patterns are you using to manage agent state in production?

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