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An AI agent may search even when it could answer from what it already knows because having a signal that a tool is unnecessary does not guarantee the agent will act on that signal. But fewer calls are not automatically better: current, obscure, multi-step, or execution-dependent questions may need outside information. The right goal is to avoid unhelpful calls without sacrificing accuracy, task completion, or timely responses.
Why an agent may search when it can answer directly
Tool use is an action choice: the agent must decide whether to answer from its existing knowledge or call a search, browser, or other tool. That decision can fail even if the model has information that would help it choose correctly.
In When2Tool, Chung-En Sun, Linbo Liu, Ge Yan, Zimo Wang, and Tsui-Wei Weng study this decision across tasks where tools are necessary and tasks where they are not. They report that tool necessity was linearly decodable from pre-generation representations across six studied models, with AUROC values from 0.89 to 0.96. That means a probe could predict the need for a tool from signals in those models; it does not mean every deployed agent reliably recognizes or follows that signal. The authors describe tool-augmented agents as tending to call tools indiscriminately, even when they could answer directly.
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The distinction matters: a model’s internal signal about whether a tool is needed is not the same thing as its stated reasoning, nor does it guarantee that its action policy will use the signal. The agent can still search because its instructions, tool policy, or action-generation process favors calling a tool.
When another tool call is worth the cost
A call is useful when it changes what the agent can responsibly know or do. The question is not simply whether the model can produce an answer, but whether that answer is adequate for the task.
- Information may have changed: Ask for current schedules, availability, recent events, or other facts that can go stale, and a search or connected data source may be necessary.
- The answer is obscure or multi-hop: A difficult information-finding task may require locating and connecting facts that are not reliably available in the model’s internal knowledge. OpenAI’s BrowseComp benchmark reports near-zero accuracy without browsing for the tested models on its deliberately difficult benchmark. That result is specific to those models and tasks, but it is a strong counterexample to a blanket rule against browsing.
- The task requires execution or verification: If the agent must retrieve a record, run a calculation in an external system, or confirm that an action succeeded, generating a plausible answer is not a substitute for using the relevant tool.
Conversely, a second search that repeats the same query without new evidence may add cost and delay without improving the answer. Whether it is redundant depends on what the call contributes, not merely on its position in the sequence.
Why fewer calls alone do not prove better performance
Call count is an incomplete efficiency measure. An agent can look efficient by skipping a useful check and returning a wrong answer. A meaningful comparison should pair resource use with task outcomes and user impact.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems- Task success and answer quality: Did the agent complete the task correctly, and did fewer calls preserve that result?
- Tool and token costs: Count calls and, where available, the associated token and tool costs. Google Research’s CATS work examines how search-agent strategies allocate resources: sequential exploration can be shallow, while parallel exploration can repeat calls and inflate cost.
- Latency and reaction time: For monitoring, measure how quickly the agent responds after the relevant event, not just how often it checked.
- Contribution of each step: RedundancyBench studies trajectory steps according to their contribution to task completion. This helps distinguish a genuinely useful call from an action that merely adds activity.
- User friction: An extra message to the user is not equivalent to an extra background tool call. In RideWay, which evaluates 58 tasks and 24 models, the fitted penalty for excess user-facing turns was about twice the penalty for excess tool calls. Annotator preference was at chance when trajectories differed only in tool-call counts. These findings are specific to the study and do not establish a universal user preference.
When comparing agents or policies, report the model, task set, tool environment, success measure, and how calls and costs were counted. A lower call count is not enough to rank one system as more efficient.
What experiments say about reducing unnecessary calls
When2Tool evaluates methods for deciding whether to call a tool, including prompt-only and reason-then-act approaches, as well as Probe&Prefill. In the study’s reported evaluations, Probe&Prefill reduced tool calls by 48% with a 1.7% accuracy loss. The best baseline at comparable accuracy reduced calls by 6%; a baseline with a similar call reduction incurred five times the accuracy loss. These are results for the models, tasks, and evaluation setup in that paper, not guaranteed savings in a production workflow.
The practical lesson is to treat call reduction as a constrained optimization problem: reduce unnecessary actions while tracking the accuracy or completion cost of doing so. The appropriate trade-off depends on the task. A system that answers routine questions directly may be preferable; one handling high-stakes or rapidly changing facts may need more verification.
Why a monitoring agent may keep searching
Long-running monitoring presents a different failure mode from answering a one-time question. If an agent is waiting for an external event, repeatedly refreshing pages or broadening searches may create activity without moving the task forward.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →SentinelBench, a Microsoft Research benchmark with 100 tasks across 10 synthetic web environments, evaluates task completion, reaction time, and resource use for long-running monitoring agents. Its authors describe continuous action—calling tools, refreshing pages, or searching for alternatives—as the default pattern they seek to examine. In this setting, a better policy may wait for a meaningful change and respond when it occurs, rather than repeatedly forcing progress. The benchmark’s synthetic environments are not a guarantee of performance in every live monitoring system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to tell whether an agent’s calls are helping
- Define what counts as success. Specify the answer or action that completes the task, including whether freshness or verification is required.
- Record the trajectory. Log each user-facing turn and tool call, its purpose, result, cost where available, and whether it changed the next decision.
- Compare policies on the same tasks. Evaluate direct-answer and tool-using behavior against the same task set and tool environment; do not compare call counts from mismatched workloads.
- Measure quality alongside efficiency. Track success, correctness, calls, costs, and—when relevant—latency or time to react.
- Inspect repeated calls for new information. A retry may be justified if the first call failed or circumstances changed. If it returned no new evidence and did not improve completion, it may be redundant.
- Set a task-specific stopping rule. For a stable, answerable question, stop when the answer is sufficiently supported. For monitoring, use an event or change condition rather than endless refreshes.
Agent tracing or LLM observability tools can help teams inspect calls and outcomes, but the measurement design matters more than minimizing a counter. The point is to see whether each action contributed to a correct, timely result.
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