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An AI agent may search even when it could answer from its existing knowledge because choosing whether a tool is necessary is a separate decision from generating an answer. Research suggests models can contain a signal about that decision without reliably acting on it. But another call is not automatically waste: current, obscure, multi-step, or execution-dependent questions may need external tools. The right goal is not to minimize calls; it is to preserve task success while avoiding calls that add no useful information.
Why does an agent search when it may already know the answer?
A tool-using agent has at least two jobs: decide whether to call a tool, then produce an answer or act on the tool’s result. Those decisions can diverge. The model may have enough information to answer, yet its action policy still selects search—perhaps because the workflow favors verification or because the decision rule does not use the model’s available signal effectively.
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In When2Tool, Chung-En Sun, Linbo Liu, Ge Yan, Zimo Wang, and Tsui-Wei Weng study when an agent should use an external tool versus answer directly. They report that tool necessity was linearly decodable from pre-generation representations, with AUROC of 0.89–0.96 across six studied models. This means a probe could distinguish tool-necessary from tool-unnecessary cases in their evaluation; it does not show that a deployed agent will consistently make the right choice. The paper’s authors characterize the broader problem this way: “Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly.”
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When a new tool call is worth the cost
Searching again is justified when it can materially improve correctness, completeness, or the ability to act. The relevant question is whether the tool can provide information or execution the model lacks—not whether the agent has already made a call.
- Information may have changed. Use a current source for live events, recent releases, prices, schedules, or other facts that can become stale.
- The answer is obscure or multi-hop. A question that requires finding and connecting scattered facts may exceed what the model can reliably recall. OpenAI’s BrowseComp page reports near-zero accuracy without browsing for tested models on its deliberately difficult information-finding benchmark, illustrating why some tasks require search.
- The task depends on execution. If the agent must inspect a page, run a query, retrieve a record, or perform an action, internal knowledge cannot substitute for carrying it out.
- Verification has meaningful value. When the cost of an incorrect answer is high, checking a source may be worthwhile even if the model can produce a plausible response from memory.
Conversely, a stable, ordinary fact that the agent can answer confidently may not benefit from a search. That is a decision to make against the task’s risk and the tool’s cost, not a blanket rule to stop browsing.
Why fewer calls do not automatically mean a better agent
A call count measures activity, not usefulness. An agent that skips necessary verification can look efficient while giving a wrong answer or failing to finish. Compare efficiency alongside task success and answer quality, and count token and tool costs where available.
In When2Tool, the authors report that Probe&Prefill reduced tool calls by 48% with a 1.7% accuracy loss in their evaluation. These figures apply to the models and tasks studied, not to production agents generally. The same work reports that its best baseline at comparable accuracy reduced calls by 6%; another baseline with similar call reduction incurred five times the accuracy loss. The trade-off makes the point: the number of calls alone cannot establish whether a policy is better.
Other evaluation work points to additional dimensions. RideWay evaluates 58 tasks and 24 models. Its authors report that the fitted penalty for excess user-facing turns was about twice that for excess tool calls, while annotator preference was at chance when trajectories differed only in tool-call count. That result is specific to the study; it cautions against treating an extra tool call as equivalent to an extra interruption for the user. RideWay proposes a success-gated efficiency measure that discounts excess effort against task-specific reference effort.
For long-running tasks, response speed can matter as much as resource use. SentinelBench contains 100 tasks across 10 synthetic web environments and measures task completion, reaction time, and resource use. Its authors warn that continuous refreshing and searching can become activity without progress: “The default model of agent behavior is continuous action: issuing tool calls, refreshing pages, searching for alternatives, or otherwise trying to force progress.” If the task is waiting for an external event, repeatedly broadening a search may not help; the agent may need to wait for a meaningful change and respond promptly when it occurs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a tool-use policy
Compare policies on the same tasks and tool environment. Report enough context for readers to distinguish a genuinely more efficient agent from one that simply avoids useful checks:
- Task success and answer quality: Did the agent complete the task correctly, and did reduced calling preserve that outcome?
- Calls and costs: Count tool calls alongside token and tool costs where available. Google Research’s CATS work highlights both cost sources and the trade-off between exploration strategies.
- Latency or reaction time: For monitoring, measure how quickly the agent responds after the relevant event, not only how often it checks.
- Contribution of each step: Ask whether a search, refresh, or other action advanced the task. RedundancyBench studies trajectory steps by their contribution to completion.
- User friction: Separate internal tool calls from questions or turns that require the user’s attention; they impose different burdens.
When publishing a comparison, identify the model, task set, tool environment, success measure, and how calls and costs were counted. Include any accuracy or completion trade-off. Without those details, “uses fewer tools” is not enough to establish better performance.
For search agents, budget exploration rather than just trimming calls
Search tasks can require exploring several possible paths. In Google Research’s COLM 2026 CATS work, Tengxiao Liu and coauthors describe a trade-off: sequential scaling can produce shallow exploration, while parallel scaling can inflate costs through repeated calls. Their point is not that one strategy always wins, but that an agent should allocate its resource budget deliberately. As the authors put it, “Sequential scaling tends to yield shallow tool use and under-exploration, whereas parallel scaling inflates cost through repeated tool calls.” The useful measure is whether the exploration budget produces better results for its cost, not whether the agent made the fewest calls.
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