Agent Optimization

Every agent consumes credits when it works. This guide covers how to understand credit usage, reduce costs, and improve agent performance through iterative refinement.

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Understanding credit usage

Credit consumption depends on what your agent does, how complex the task is, and which LLM you use. Every case is unique, but here are examples to give you a sense of scale:

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Reducing costs

Several factors affect how many credits your agent consumes:

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1. Frequency of use

Running a task every few minutes versus once an hour makes a significant difference. Consider whether your agent really needs to run that often, or if a less frequent schedule would work.

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2. Size of context

The more information your agent reads and processes, the more credits it uses. Trim context to only what’s essential for the task.

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3. Where you store data

Store reference information in agent documents that the agent can selectively retrieve, rather than including everything in the instructions. With the default “Summary” visibility, the agent knows documents exist but only reads them when relevant—keeping context focused.

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4. Complexity of instructions

Simplify logic and reduce unnecessary processing loops. Clear, concise instructions often perform better than lengthy, detailed ones.

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5. Model selection

By default, agents use the latest version of Claude Sonnet—a high-performance model optimized for agentic behavior. If cost is a concern and your use case allows it, considering switching to a cheaper model. See Model Selection for details.

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