agent-cost-controllisted

# Agent cost control Cost scales with tokens processed, and most waste is context that was never needed. Controlling it is mostly about what you send rather than about using a weaker model. ## Method 1. **Reduce context before reducing model.** Trimming irrelevant files preserves quality while cutting cost; a weaker model may not (see context-compression). 2. **Use prompt caching for stable prefixes.** Instructions and reference material that repeat across turns can be cached at substantially lower cost (see prompt-caching). 3. **Match the model to the task.** Routine mechanical edits do not need the strongest model, while architectural work does (see agent-progressive-disclosure). 4. **Start fresh sessions for new tasks.** Continuing in a long session carries the entire history into every subsequent turn. 5. **Avoid re-reading unchanged files.** Repeated reads of the same large file across a session are pure duplication. 6. **Bound autonomous loops.** An agent iterating without a cap can consume a large budget on a task it cannot complete (see agent-loop-until-exhausted). 7. **Measure cost per task, not per token.** A more expensive model finishing in one pass often costs less than a cheap one iterating five times. ## Boundaries Cost optimisation must not compromise verification on consequential work (see agent-human-checkpoint). Pricing and caching behaviour differ by provider and change. The engineer's time is usually more expensive