Better AI agents may call the model less often
A strong AI agent setup does not send every step to a . Even with messy, very large scientific data, the choice of tool or parser for a file is often clear enough to handle with fixed rules. Tool choice, retries, output parsing, and deciding when to stop often need ordinary code, not model judgment.
Sending those steps to the model makes the system slower and harder to debug, because the failure may be hidden somewhere inside a chain of uncertain . If a broken step can only be fixed by changing the prompt, the system may be a thin wrapper rather than a solid . The model should be used for real , mixed signals, and cases where no rule handles the situation well.
Once the is mapped out, the part that truly needs the model may be smaller than expected.
Key points
- Tool choice, retries, output parsing, and stopping rules can often be handled by code.
- Too many can make an agent slower and harder to debug.
- Prompt fixes may hide weak .
- The model is most useful when the task involves real or conflicting signals.
- Agent cost can drop when clear steps are moved out of the model path.