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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesTo reduce an AI agent’s token use safely, first measure usage across a complete task, then remove irrelevant context and unnecessary output or calls. Preserve the information the agent needs to make correct decisions, and compare task success—not just token totals—before and after each change. Prompt caching can lower the cost of processing repeated input, but it does not remove that input from the request.
Measure token use across the whole task
A task may involve several model calls. Each call can include instructions, tool definitions, conversation history, retrieved files, the user’s request and results returned by tools. Outputs include generated text and tool-call arguments; some systems also report reasoning tokens. A short final answer therefore does not necessarily mean the agent used few tokens.
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Use the target API’s usage fields to record actual input, output and cached-token counts. Tokenization varies by model and encoding, and text-only estimates may miss costs from message structure, tools, schemas, images or files. OpenAI’s token guide explains token counting, while its agent usage documentation describes observing usage across agent runs.
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For a meaningful baseline, include every call and retry needed to finish representative tasks. Track relevant tool or third-party costs separately as well: cutting model tokens may not lower total task cost if it adds other work.
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Remove irrelevant context without losing task state
Filter retrieved material
Pass only retrieved passages that bear on the current decision. Large retrieval results can consume tokens even when the agent uses only a small portion. Check that filtering retains relevant constraints, exceptions and source details; a smaller context is not better if it removes a fact needed for a correct answer.
Clean tool results
Trim irrelevant or repetitive portions of tool output before adding them to the next model call. Keep the fields and surrounding context the agent needs to interpret the result. Avoid truncating blindly: a cut-off value, error message or qualification can change the next action.
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Manage long histories carefully
When earlier turns are no longer useful verbatim, retain the facts, constraints and recent interactions needed for the next decision, and summarize older material. Summaries themselves can omit important requirements, so test them against representative tasks rather than assuming a shorter history is equivalent.
A 2026 preprint by Microsoft-affiliated authors tested context policies on a 50-task hotel-expense benchmark averaged across five runs. Full-context retention achieved 71.0% complete itemization, using 1,480,996 tokens and 14.56 hours. Keeping the last five tool calls plus automated summarization achieved 91.6% complete itemization and 99.64% average amount itemized, using 553,374 tokens and 5.79 hours. The authors report that this configuration used 62.7% fewer tokens and 60.2% less time than full-context retention. These results concern that benchmark and do not establish that a five-call window is best for other agents or workflows. Read the preprint.
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Use prompt caching to reduce repeated-input cost
Put reusable instructions and definitions before request-specific details, and avoid changing the shared prefix unnecessarily. When a provider’s cache rules allow it, a matching prefix can reuse processing for repeated input. Eligibility, cache lifetime and billing differ by provider, and a session by itself does not guarantee a cache hit. Cached input remains part of the request and is billed at the applicable cached rate; compare full-task cost rather than cache percentage alone.
OpenAI states that “A high cached-input percentage does not measure savings on the total task cost.” OpenAI’s prompt-caching guide explains its caching behavior, and its agent usage guide covers usage measurement. As a provider-specific example, Anthropic reports that during a day of its own observed agent-loop traffic, the median loop read 84% of input from cache, while the top 10% read 94% or more. This is vendor-reported experience, not an independent benchmark or a promise of results for another workload. Anthropic’s cost and intelligence guidance describes the observation.
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Reduce unnecessary output and model calls
Ask for only what the next step needs
Specify the required level of detail and omit commentary that downstream steps do not use. A structured response can help when the next step needs predictable fields. Keep enough explanation, evidence or intermediate detail to make the result usable and checkable.
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Combine sequential work only when it stays clear
Combining subtasks can avoid repeated instructions and calls, but a single larger prompt may be harder to follow or verify. Compare the combined workflow with the separate calls on real tasks; retain the change only if it saves resources without increasing omissions or errors.
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Do not treat latency and token cost as interchangeable goals. OpenAI’s latency guidance says that in ordinary cases, cutting 50% of a prompt may improve latency by only 1–5%; that is a latency observation, not a general estimate of cost savings. It also cautions: “Unless you’re working with truly massive context sizes (documents, images), you may want to spend your efforts elsewhere.” Read OpenAI’s latency guidance.
Benchmark savings against task quality
Change one part of the workflow at a time and compare the same representative tasks before and after. Log enough to see whether a token reduction shifted work elsewhere or harmed results.
- Total input and output tokens, including cached usage.
- Model-call and retry counts for each completed task.
- Task completion, errors, omissions and any task-specific quality measure.
- Latency and relevant tool or third-party costs.
Keep an optimization only when its savings meet the application’s needs without unacceptable quality loss. There is no universally best history-pruning window or caching setup: the right choice depends on the agent, provider rules, model and task. The preprint’s authors also identify broader generalization to other enterprise domains, model families, deployments and decoding settings as future work.
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