Cut coding agent token use with better tool output

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Before an AI coding agent writes a single line of code, it has already spent tokens. For example, on source files, ticket descriptions, build logs, quality findings, and dependency alerts. Most of what you pay for isn’t the pull request; it’s everything the agent read to get there.

Teams often focus their cost controls on model choice, prompt length, and request limits. Those are worthwhile levers, but a less visible one sits in the interfaces between agents and developer tools: the format of the data returned to the model.

“Token costs are shaped not only by what coding agents read, but also by how development tools package that information.”

When a tool returns a long list of similarly shaped records, sending verbose JSON can make the agent pay repeatedly for field names, quotation marks, and structural syntax. The content is useful; much of the representation is not. For agentic development workflows, the output format is an engineering decision, not a cosmetic one.

This does not mean every integration should abandon JSON. JSON remains a broadly supported interchange format and can be the more compact choice for nested or irregular data. The practical question is narrower: when a model needs to consume a large, uniform collection, can the tool return the same information in a representation designed for that shape?

Why structure becomes part of the token bill

Consider an issue list. Each entry may include an identifier, rule, severity, file, line number, message, status, and estimated remediation effort. In conventional JSON, those labels appear for every …

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