When an agent’s token bill climbs, the usual reaction is to lower the token budget. That treats the bill, not the cause. Token use records what the agent did. The cause is usually a path in which a model call, tool call, retry, or handoff feeds into another costly step with no effective stop. Find that path, bound it, and token use usually falls as a side effect. Capping tokens alone rarely fixes it.
A token count cannot show what the agent did
A token total is an aggregate. It tells you that a run was expensive but not which step caused the expense. Two runs with identical totals can have completely different causes: one may have made a single long reasoning pass, while the other may have called the same search tool eleven times and passed a growing transcript back into the model on every turn.
To see the difference, you need a trace. OpenAI’s agent tracing documentation describes traces that record model responses, tool calls, handoffs between agents, inputs and outputs, duration, and status. Databricks’ MLflow observability guidance describes the same kind of step-level view for agent applications. Those records answer questions a token count cannot: which tool was called, how many times, in what order, what it returned, and whether the agent ever reached a stopping point.
The AWS Well-Architected Agentic AI Lens states the cost mechanism directly: “Agent reasoning cycles consume tokens through iterative plan-execute-verify-reflect loops, and multi-agent coordination adds multiplicative overhead.” The practical reading is that cost grows with the number of cycles and with each added agent, so cost questions are really execution questions.
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Iteration is normal; unbounded feedback is the defect
Looping is not a bug in itself. Many agent designs legitimately plan, act, check the result, and act again. The problem appears when a feedback path repeatedly invokes an expensive or state-growing operation and nothing effective limits it. The loop then turns one user request into many model calls, many tool calls, and sometimes many changes in external systems.
Four patterns account for most of the cases worth investigating:
- Repeated actions. The agent calls the same tool with the same or nearly identical arguments because the result never satisfies its success check.
- Retry amplification. A failed tool call is retried automatically, and each retry re-runs the model step that requested it.
- Handoff ping-pong. Two agents return work to each other, and neither has a rule for when the task is finished.
- State growth. Each turn appends the full prior context, so every later call costs more than the one before it, even when the number of steps is modest.
The fourth pattern is easy to miss because the step count looks reasonable. A run with eight steps can cost several times more than a run with twenty if the later steps carry the accumulated history.
What a useful trace should show you
Before changing prompts or budgets, confirm that your trace records the following for every run:
- Each model call, with its input size, output size, and the tokens it consumed.
- Each tool call, with arguments, returned result, duration, and any error.
- Each retry, and the step that triggered it.
- Each handoff, with the sending agent, the receiving agent, and the context passed along.
- The final status and whether the agent stopped because it succeeded, hit a limit, or was interrupted.
If a trace lacks any of these, the missing piece is usually the one that explains the cost. Add instrumentation before you tune anything.
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How to diagnose an expensive run
Compare two runs: one representative successful run and one representative failed or unexpectedly expensive run. Working from the pair, which is more informative than either run alone, follow this sequence.
- Open the full trace for each run and list the steps in order.
- Mark repeated or near-repeated actions. Note whether the arguments changed between attempts.
- Check retries and handoffs. Identify which step started each one and whether it had an exit condition.
- Plot input tokens per step. A rising curve across steps points to state growth rather than a single expensive call.
- Record the final outcome of each run, so that cost is read alongside whether the task was completed.
- Name the component responsible: the behavior instructions, the tool definitions, routing between agents, guardrails, retry logic, or the execution bounds.
OpenAI’s documentation describes trace grading for workflow-level questions such as whether the right tool was selected, whether a handoff happened when it should have, and whether an instruction was violated. Those checks can be run against trace data to find the same patterns at scale rather than one run at a time.
Bound the execution path at runtime
Telling the model to stop is not a bound. A bound is a condition the runtime enforces regardless of what the model decides. The AWS guidance on agentic AI calls for explicit termination conditions, iteration caps, and session token budgets. The same guidance describes confidence-based exits, which let an agent stop once it has enough evidence to answer, and selective reflection, which means running an extra self-check only where the task warrants it rather than on every step.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallAWS states the intended outcome: “Agent reasoning cycles are bounded by explicit termination conditions and confidence-based exits, so token consumption is predictable and proportional to decision complexity.” The sentence describes a design goal. Whether a given agent reaches it depends on whether each of the following is enforced in code:
- Iteration cap. A maximum number of model-and-tool cycles per task, with a defined result when the cap is reached, such as returning partial output with a status flag.
- Repeat detection. A rule that stops or escalates when the same tool is called with the same arguments more than a set number of times.
- Session token budget. A ceiling on total tokens for one request or session, enforced by the runtime.
- Scoped handoff context. The receiving agent gets the information it needs for its step, not the full transcript of the sender.
- Retry limits. A fixed retry count for each tool, with backoff and a clear failure path.
AWS’s maturity guidance describes enforcing some of these limits at the control plane, outside the agent’s own reasoning. That placement matters. A limit the model can talk itself past is weaker than one the surrounding system applies.
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Turn the fix into a repeatable test
A fix that works once on a single run is not yet a fix. OpenAI’s agent evaluation documentation and Databricks’ trace-to-monitoring description both point toward the same loop: take real traces, turn recurring failures into cases, grade the agent against them, change the system, and watch production for the failure to return. The steps look like this:
- Inspect representative traces, including both successful and expensive runs.
- Collect feedback on the failures, from logs, user reports, or reviewers.
- Curate the failing cases into a dataset with stated inputs and expected outcomes.
- Write or tune graders. A grader should encode the user-relevant success criteria, not a single required sequence of steps, because several different paths can complete the same task correctly.
- Change the implicated component, then rerun the dataset and review quality and cost together.
- Monitor production and send new failure cases back into the dataset.
Test workflows that change state
For agents that modify files, records, or other environment state, a grader that reads only the final text response can miss the real failure. The agent may report success while having repeated a write or left a partial change. Test these workflows with the tools connected and with realistic state, so the grader sees what the agent actually did.
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Agent runs are not deterministic. The same case can pass on one trial and fail on another. Running several trials per case gives a more honest picture than one pass, and it prevents a single lucky run from being recorded as proof that the fix worked.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure quality alongside resource use
A lower token count is not a success measure by itself. An agent that stops early may be cheaper and wrong. The AWS lens lists latency, throughput, quality, and efficiency as separate dimensions, including tool invocation efficiency and task completion time. Record them together for each run.
| Measure | What to record per run | Signal that the loop is still present |
|---|---|---|
| Tokens | Input and output tokens per model step and in total | Input tokens rising step over step |
| Tool invocation efficiency | Number of tool calls and how many were repeats | Same tool, same arguments, called again |
| Latency and completion time | Total duration and time to final status | Long runs that end at an iteration cap rather than a result |
| Task quality | Grader result against user-relevant criteria | Cheaper runs that score lower on the criteria |
| Final status | Completed, capped, escalated, or failed | A high share of runs ending at a cap |
Read the table as a pair of questions for every change: did cost fall, and did the outcome hold?
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What the evidence does and does not establish
The clearest quantitative evidence on this topic comes from a 2026 arXiv preprint on IAL-Scan, a static-analysis tool for finding infinite-loop failures in LLM-agent code. Its authors report that they analyzed 6,549 LLM-agent repositories, flagged 74 potential findings, manually confirmed 68 loop failures across 47 projects, and reported 91.9% precision. Those figures describe that study’s dataset and method. They show that such loops can be detected in code at scale. They do not give a rate of infinite loops in deployed agents, and they do not say what share of any team’s token spend comes from loops.
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Choosing tooling for the loop
Tools differ in how they support the diagnosis-and-test cycle, so compare them on the capabilities that the cycle depends on:
- Visibility across the full run, including tool calls and handoffs, not just model responses.
- Cost and latency attached to steps, so token growth can be located.
- Trace grading and repeatable datasets, so a fixed failure can be checked again later.
- Enforcement of execution bounds, or at least the ability to implement them in your own runtime.
- Export and integration, so traces can move into your monitoring and data stack.
- Data governance and operational fit, including where trace data is stored and who can read it.
OpenAI’s agent tracing and evaluation documentation and Databricks’ MLflow observability guidance are representative examples of this category. Treat them as options to evaluate against these axes, not as a recommendation for every team.
Taken together, the approach is simple to state and takes discipline to apply: read the trace, find the path that repeats or grows, bound it in the runtime, and test the fix against the criteria that define a successful outcome.
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