To reduce an AI agent’s token use safely, first measure the full cost of representative tasks, then remove repeated or irrelevant context, control unnecessary outputs and calls, and test every change against task completion and errors. Prompt caching can reduce the price of eligible repeated input, but it does not remove those tokens from the request.
Measure token use across the whole task
A multi-step agent can make several model calls before it returns a short answer. Counting only the final response misses much of the work. A task’s usage can include system and developer instructions, tool definitions, conversation history, files, the user request, tool results, generated text, tool-call arguments, and reasoning tokens. Retries add more usage, and tools or third-party services may have separate costs. OpenAI recommends inspecting agent usage across runs and calls in its agent observability documentation; its token guide explains token counting.
Start with the API’s actual usage fields for the model and provider you use. Tokenization varies by model and encoding, and a plain-text estimate may not fully represent message structure, tools, schemas, images, or files. Record per-call input and output, cached input where reported, number of calls, retries, latency, and task outcome. Sum across the complete task rather than judging by one call or a cache percentage.
Build a representative baseline
Run a fixed set of real tasks before changing the agent. Include routine cases and the edge cases where it must preserve constraints, recover from tool errors, or use older conversation details. Track whether each task completed correctly, what it cost in tokens and other metered services, and how long it took. This baseline lets you distinguish genuine efficiency gains from a smaller but less capable agent.
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Remove context the next decision does not need
Repeatedly sending irrelevant or stale material is a common source of avoidable input tokens. Filter retrieved passages for relevance, and clean tool results before adding them to the next prompt. For example, retain the fields an agent needs from a long tool response rather than forwarding every log line or unrelated record.
Long conversations need care: preserve facts and recent interactions required for the next decision, but consider summarizing older material that no longer needs to remain verbatim. A summary is not automatically safe. It can omit a constraint, exception, or prior decision that later becomes important, so compare summarized and full-history behavior on representative tasks before adopting it.
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Prune history based on evidence, not a fixed rule
A 2026 preprint by Microsoft-affiliated authors studied a 50-task hotel-expense benchmark, averaging results across five runs. Full-context retention achieved 71.0% complete itemization with 1,480,996 tokens and 14.56 hours. A policy retaining the last five tool calls plus automated summarization achieved 91.6% complete itemization and 99.64% average amount itemized with 553,374 tokens and 5.79 hours. The authors report 62.7% fewer tokens and 60.2% less time for that configuration than full-context retention (Lodha et al., preprint).
Those results show that context management can improve both efficiency and task performance in a particular workflow; they do not establish that a five-call window is best for other agents. The paper identifies broader testing across enterprise domains, model families, deployments, and decoding settings as future work. Choose a history policy by testing the information your own tasks require.
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Reuse stable prompts with caching, but separate cost from token volume
When requests share substantial instructions or definitions, put that stable material before the changing request details and avoid rewriting it unnecessarily. Providers may cache processing for matching eligible prefixes, subject to their own requirements and cache lifetimes. A long-running session by itself does not guarantee a cache hit; check the provider’s usage fields and caching documentation. OpenAI describes its eligibility and behavior in its prompt caching guide.
Caching is different from removing context. The request still contains the cached input, and cached tokens remain billable at the applicable cached rate. OpenAI cautions that “A high cached-input percentage does not measure savings on the total task cost” in its agent usage documentation. Compare total cost for the task, including uncached input, output, retries, and relevant tool charges—not the cached share alone. Cache rules and rates vary by provider, so verify the behavior for the model and API you actually use.
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Reduce unnecessary output and model calls
Tell the agent what the next step needs, not everything it could say. If another component consumes the result, a concise structured response can reduce generated tokens and make parsing more predictable. Specify required fields and acceptable values where useful, but retain enough explanation or evidence for the downstream step to act correctly.
Consider combining sequential subtasks into one call when the combined instruction remains clear and bounded. This may avoid repeated prompt overhead, but a larger, harder-to-check response can create omissions or errors. Keep separate calls when they provide useful control points, validation, or recovery. Measure the full task either way, including retries after a failed or incomplete result.
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Token reduction does not guarantee a proportional latency improvement. OpenAI’s latency guidance says cutting 50% of a prompt may improve latency only 1–5% in ordinary cases, and notes that “Unless you’re working with truly massive context sizes (documents, images), you may want to spend your efforts elsewhere.” These are latency observations, not universal estimates of cost savings; see OpenAI’s latency optimization guidance.
Benchmark savings without losing task quality
Change one part of the context or call strategy at a time, then rerun the same representative task set. Compare the results against the baseline using measures that cover both efficiency and success:
- Usage: total input and output tokens, cached input if available, calls, retries, and relevant tool or third-party charges.
- Quality: completion rate, correctness, required-field coverage, and errors that matter to your application.
- Performance: end-to-end latency, not just the response time of a single model call.
- Operational cost: implementation and maintenance effort, plus how portable the change is across your providers and models.
Keep an optimization only if its savings meet your application’s needs without an unacceptable decline in quality. There is no universally best context window or caching setup: the right balance depends on the information each task needs, provider rules, and how costly mistakes are.
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