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To reduce token usage in a multi-agent AI workflow, measure every agent call first, then cut duplicated context, unnecessary delegation, oversized tool definitions and results, and stale conversation history. Prompt caching can lower the cost of repeated input, but it does not remove those tokens from context. Compare complete workflow cost and task quality before and after each change; a high cache-hit rate or lower token count on one call is not proof of an overall saving.
What to measure before changing the workflow
A multi-agent run is more than the root agent’s final request. Its total usage can include root-agent calls, every subagent call, tool definitions and results, generated tool arguments, retries, and the synthesis work that combines agent findings. If you count only the final call, you can miss the largest sources of consumption.
Start with a baseline using a representative set of tasks. Record usage at the call level and group it by root agent, subagent, retry, and tool operation. Include input and output separately. OpenAI’s observability and usage guide identifies instructions, tool definitions, conversation history, user input, files or images, and tool results as possible input contributors. Generated text, tool-call arguments, and reasoning contribute to output; for the documented OpenAI API, reasoning tokens are billed as output tokens.
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- Input: Record the prompt and context sent to each call, including repeated instructions, schemas, files, and results.
- Output: Capture generated text, tool-call arguments, and any provider-reported reasoning usage.
- Workflow overhead: Mark retries, agent handoffs, tool roundtrips, and the final synthesis call so they are visible rather than folded into an unexplained total.
- Outcome: Note whether the task completed correctly, whether required evidence or constraints were preserved, and how long it took.
Use the provider’s usage fields and traces where available, but interpret them carefully: some fields may be best-effort or delayed. Compare the complete workflow, not a single model call. Include relevant tool, sandbox, or third-party service charges in your internal cost estimate as well as model usage. Provider-specific fields and billing rules differ, so keep the accounting method consistent when comparing runs.
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For a practical baseline, select several ordinary tasks and run the existing workflow without changing its prompts or agent plan. Save the per-call records and outcomes. Then change one major factor at a time, or clearly document a bundle of related changes, and compare the new totals against that baseline. There is no broadly applicable savings percentage established for multi-agent workflows; results depend on the workload, models, tools, and implementation.
It helps to distinguish three questions in your records: how many tokens were used, how much those tokens cost under the applicable model and cache rules, and how much the complete task cost after tools and retries. Those figures are related but are not interchangeable. A change may lower uncached input charges while leaving the context length unchanged, or lower tokens in one call while adding another call or a retry. Use the provider’s actual usage and billing fields rather than estimating from the final answer’s length alone.
Choose the optimization that targets the largest cost
Different interventions address different sources of usage. Use the baseline to choose a target instead of assuming that caching, fewer agents, or shorter prompts will fix the dominant cost.
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| Fewer or more selective agents | Duplicated instructions, agent calls, and synthesis work | May reduce parallel coverage or increase elapsed time | Small, sequential, or externally constrained tasks |
| On-demand tool discovery | Tool schemas loaded before they are needed | An extra lookup turn may add latency | Large tool catalogs where most requests use only a subset |
| Programmatic tool calling | Repeated tool-call and result roundtrips | Requires supported execution and carefully designed scripts | Repeated chains of small tool operations |
| Prompt caching | Repeated-input processing cost for eligible matching prefixes | Does not shrink context; eligibility and pricing vary | Stable instructions, tool schemas, or reusable reference material |
| Context editing or compaction | Stale accumulated history and oversized prompts | Can discard useful context or disrupt cache reuse; validate quality | Long-running conversations with obsolete results |
| Prompt templates and structured handoffs | Prompt duplication and verbose agent reports | Overcompression can omit necessary constraints or evidence | Repeated task types and bounded specialist assignments |
These options are workflow choices, not guaranteed savings. An intervention that lowers token usage but causes more retries or poorer results may increase the cost of a successful task.
For example, if traces show that most input comes from a large tool catalog included on every call, deferred tool discovery is a more direct test than shortening the final response. If the catalog is small but every agent receives the same long task history, tighten the handoff or context instead. If tool results are already compact and agents perform independent research, reducing parallel coverage may be the wrong trade. The right first change follows from the source of usage, not from a universal ranking of techniques.
Use subagents only for work that benefits from delegation
Adding agents does not automatically save tokens. Each specialist may receive instructions and context, make its own calls, and require additional synthesis. OpenAI’s Multi-agent API guide explicitly warns that adding subagents can increase token usage. It identifies independent workstreams as a good reason to delegate and cautions against subagents for a single ordered chain of reasoning, frequent writes to shared mutable state, or work dominated by one slow external operation.
Delegate bounded, independent work
Parallel investigation can be useful when work can be split cleanly—for example, examining separate components of a codebase or checking distinct sources. Each agent should have a defined question, the specific context relevant to it, and only the tools needed to complete its assignment. Ask it to return a compact result that identifies its conclusion, supporting evidence, unresolved issues, and any next action the root agent needs to take. This design follows from the fact that every agent call consumes context and may add synthesis work; it is not a measured promise of savings.
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A practical delegation test is whether each specialist can make progress without waiting for another specialist’s answer or modifying shared state needed by the others. If the work is separable, parallel calls may improve coverage or elapsed time, but the root agent still needs enough evidence to integrate and check their findings. Avoid sending every specialist the entire conversation by default: include the task, relevant constraints, and material specific to that assignment. If shared context is genuinely necessary, include it rather than risk an incomplete result.
Keep one agent for short or ordered tasks
A short request, a tightly dependent chain of reasoning, or a task waiting on one slow external operation often has little to gain from parallel agents. In those cases, delegation can duplicate context and add handoff and synthesis calls without shortening the work. For shared mutable state, extra agents may also create coordination work rather than independent progress.
Evaluate agent count by total task cost and outcome
Compare the same representative task with and without the specialist agents. Count all of their calls and the root agent’s synthesis, then check whether parallel coverage improved completeness or elapsed time enough to justify that usage. If an agent’s result is consistently unused, duplicative, or too broad to integrate, revise its assignment or remove it.
Do not judge the comparison on latency alone. Parallel agents can finish sooner while consuming more tokens; a single agent can use fewer tokens while taking longer. Decide which balance matters for the application, and preserve a quality check such as whether the final answer contains the required evidence, covers the assigned work, and respects the task’s constraints.
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A large tool catalog can consume input tokens before an agent has done useful work. Tool results can then accumulate in conversation history and be sent again on later calls. Reduce both sides of this overhead.
Load only relevant tools
When a workflow exposes many tools but a typical task needs only a few, make tool definitions available on demand rather than putting the full catalog into every early request. OpenAI documents deferred tool loading in its prompt-caching guide. Anthropic’s tool-context documentation describes tool search that keeps definitions out of context until they are requested. The lookup can add a turn and latency, so it is most attractive when the catalog is large and requests use only a small share of it.
On-demand discovery is not automatically best for every workflow. If most tasks use the same small set of tools, loading those definitions directly may avoid an unnecessary discovery step. If tasks vary widely across a large catalog, search or deferred loading can avoid repeatedly paying the context cost for irrelevant schemas. Check the tool path end to end: include the discovery call and any definitions it returns in the comparison.
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Make tool results useful to the next step
Return only the fields required for the next decision instead of forwarding an entire response when a few values will do. If the provider supports programmatic tool calling, consider batching repetitive small operations in a script: Anthropic describes this as a way to avoid placing intermediate results into separate conversation-history roundtrips. It requires a supported execution environment and careful script design, and is not suitable for every operation.
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For instance, when a sequence of small calls can be executed together, a script can pass the relevant intermediate values directly between operations rather than making the model receive and restate each result. This approach is most useful when the operations are repetitive and their intermediate outputs do not require model judgment. If the model must inspect a result, choose a branching decision, or preserve an important explanation, removing that intermediate information could harm the task rather than improve it.
After a result has served its purpose, remove or edit it from the context where the provider supports that operation. Keep source data available through the appropriate tool or storage mechanism if a later step still needs it; trimming conversation history should not silently remove information the workflow depends on. Test the resulting tool path to catch cases where a compact result omitted a necessary field.
Use prompt caching for repeated, matching input
Prompt caching addresses the cost of processing repeated input, not the size of the prompt the model sees. It is useful when requests share an unchanged prefix such as stable instructions, tool definitions, or reusable reference material. Keep that common content early and append task-specific or changing data afterward where the provider’s interface allows it.
For OpenAI, cache reuse depends on the rendered prefix matching; changing earlier prompt content or relevant settings can prevent reuse. See the OpenAI prompt-caching documentation for current requirements. Anthropic makes the distinction explicit: caching reduces what is paid for eligible repeated input but does not reduce the number of tokens in context. For Gemini, Google says implicit caching is enabled by default for Gemini 2.5 and newer models and recommends placing large common content early and sending similar prefixes close together. Its model-specific details and thresholds can change; check the current Gemini context-caching documentation before estimating eligibility or savings. Google’s caching page was last updated October 9, 2026, so check the live model table when implementing rather than carrying an old threshold forward.
Stable prefixes are a design choice, not a reason to keep unnecessary material in every request. A long common prefix may be cacheable and still use context, and the changing portion of the prompt still has to be processed. First decide whether the reusable content is needed; then arrange necessary common content consistently for the provider’s cache behavior. Do not add a large reference block merely to create a cache hit.
A high cached-input share is not, by itself, a savings calculation. Compare total task usage and cost before and after, including uncached input, output, retries, and added calls. Keep model and API eligibility in view, since caching behavior and pricing vary by provider and model.
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Bound long conversations with editing or compaction
In a long-running workflow, old tool results and discussion can crowd out the current task even when they are no longer useful. Remove stale material or use a provider-supported context-management feature. OpenAI’s Responses API compaction guide describes server-side compaction configured with a threshold. The returned compaction item carries forward key prior state using fewer tokens.
Preserve state the next step actually needs
Before trimming or compacting, identify the constraints, decisions, identifiers, and unresolved work needed by the next call. Validate that the resulting context still contains them and that the workflow produces acceptable results. Compaction is a mechanism for reducing accumulated context, not a guarantee that every detail important to a task will be retained.
Consider a handoff checklist for the state that must survive: the current goal, decisions already made, identifiers or references needed to continue, open questions, and any constraints that still apply. This keeps the next step from having to reconstruct the task from a long transcript. Keep details that are needed to verify a finding, and remove material that is both obsolete and recoverable if needed. The compacted state should serve the next operation, not attempt to preserve a shortened copy of every prior turn.
Check cache effects after compaction
Compaction can change the prefix of a subsequent request and reduce cache reuse on the first request afterward. Compare total input cost across the workflow before and after compaction rather than treating a smaller context as an automatic overall saving. For a very short task, the setup and changed prefix may make compaction unhelpful; test it on the long runs where accumulated history is a meaningful share of input.
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Repeated task types often accumulate near-duplicate prompts. OpenAI’s agent-building guide describes using a flexible prompt template with policy variables rather than maintaining separate prompts for each use case. Reuse can make prompt maintenance and evaluation simpler. It does not establish a quantified token reduction, so measure the effect in your workflow.
Templates are especially useful when use cases differ in a few known instructions but share a common task structure. Keep stable instructions in the shared template and pass only the policy or task values that vary. That can also make it easier to keep a repeated prefix consistent where caching is available. Still, avoid turning a template into a catch-all prompt with every possible instruction and tool: include only what the current agent needs.
For handoffs, give specialists a concise output shape that preserves what the root agent needs rather than asking for an open-ended report. A useful structure can include:
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- Finding: The direct answer to the assigned question.
- Evidence: The minimum details or source references needed to assess the finding.
- Uncertainty: A specific gap, conflict, or assumption the root agent must resolve.
- Next step: An action only if the broader task requires one.
Do not compress away evidence, constraints, or caveats that affect the decision. An output schema helps focus a report; it cannot replace the root agent’s need to verify that the result is sufficient. If the root agent repeatedly asks specialists for missing information, the handoff may be too terse or the assignment may be unclear. Track those follow-up calls: the shortest first report is not necessarily the most efficient handoff.
Run a controlled optimization cycle
Make optimization a repeatable process rather than a one-time prompt-shortening exercise. The aim is lower total cost for an acceptable result, not the smallest possible token count in isolation.
- Choose representative tasks. Include ordinary cases and any recurring long or tool-heavy cases that materially affect usage.
- Capture the current workflow. Save per-call input and output usage, agent and tool traces, retry counts, elapsed time, and task outcomes.
- Find the largest avoidable contributor. Determine whether the issue is duplicated agent context, always-loaded schemas, repeated tool results, stable repeated prefixes, or obsolete history.
- Change the matching part of the workflow. For example, selectively delegate independent work, defer seldom-used tools, shorten handoff results, stabilize a cacheable prefix, or compact a long context.
- Run the same task set again. Compare total workflow usage and cost, not just one call or cached-input percentage. Keep model, task, and other conditions as comparable as possible.
- Check quality and operational trade-offs. Confirm that evidence and constraints survive, results remain correct, and latency or failures have not become unacceptable.
- Keep, revise, or revert. Adopt the change only when the measured whole-task result is worthwhile for the quality and speed your use case requires.
Provider usage details evolve, and some fields may arrive late or be estimates. Recheck the relevant provider documentation when changing models or APIs, and avoid applying a threshold, cache rule, or billing assumption from one provider to another.
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Frequently Asked Questions
Does prompt caching reduce the number of tokens in a multi-agent workflow?
No. Prompt caching can reduce the cost of processing eligible repeated input, but cached content still occupies context and remains part of input usage. It is different from removing stale context or shortening a prompt. Compare whole-task cost rather than treating a cache-hit share as the saving.
When should I use multiple agents instead of one?
Use subagents when the work divides into bounded, independent tracks that benefit from parallel exploration or separate context. A short task, a single ordered reasoning chain, frequent shared-state updates, or work waiting on one slow external operation may not benefit. Count each subagent’s calls and the root agent’s synthesis when comparing.
What should I count when estimating the cost of an agent workflow?
Include input and output usage for the root agent, all subagents, tool calls, synthesis, and retries. Also account for relevant tool, sandbox, or third-party service charges. Provider usage fields can be best-effort or delayed, so use consistent records and compare complete tasks rather than only the final model call.
Can I save tokens by shortening every agent handoff?
Shorter handoffs can help when they remove irrelevant detail, but overcompression can omit evidence, constraints, or caveats. Ask for a focused finding, necessary evidence, uncertainty, and next action when needed. If the root agent repeatedly requests missing information, the initial handoff may be too terse.
How do I choose between on-demand tools and programmatic tool calling?
On-demand discovery can reduce schema context when a large catalog is only occasionally used, at the cost of a lookup turn and some latency. Programmatic tool calling can suit repeated chains of small operations when the provider supports it and intermediate results need not be separate model-visible roundtrips. Include the added calls, execution requirements, and quality checks in your comparison.
The Bottom Line
Start with per-call usage and traces, then target the largest avoidable source of cost: duplicated agent work, always-loaded tool schemas, repeated tool results, or stale history. Use caching for stable matching prefixes, not as a substitute for context reduction. Keep changes only when they lower complete workflow cost while preserving quality and acceptable latency.
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