Setting Budget Thresholds for a New AI Agent With No Spend History

Why a new agent needs a different approach

A newly deployed agent has no baseline for token consumption, tool-call volume or downstream API usage. Any number chosen on day one is an assumption. Limits set too loosely allow runaway loops, excessive tool calls or unintended actions. Limits set too tightly interrupt legitimate work and give teams a reason to bypass the controls.

The practical answer is to treat threshold-setting as a staged runtime policy design problem, not a forecasting exercise. NIST's agent identity design tenets point the same way. They state that organizations define risk thresholds and tolerances that, when reached, initiate human review of agentic actions. They also note that agents operate at a scale and velocity that humans and most deterministic automation do not, so agent-specific metrics, thresholds and policies are needed rather than reused human-user limits.

The starting point is therefore a bounded, conservative policy that is expected to change, with a defined mechanism for changing it.

Choose the dimensions to bound

Bound the agent along four dimensions. This structure is an editorial design choice, not a standard. Each dimension catches a different failure mode, and a single limit on one of them rarely covers the others.

• Per interaction: Caps on input and output size for a single model request. This limits the effect of one malformed or adversarial prompt.
• Per tool call: Constraints on a single call: which tool, which operations, which parameters, and whether it reads or writes. This is where unintended actions are most directly contained.
• Per session: Cumulative limits on tokens, tool calls and external API invocations within one session. These catch loops that stay within every per-call limit.
• Per time window: Request rate and volume over a defined window. These bound total exposure when many sessions run at once.

Derive conservative starting values without history

With no usage data, derive initial values from the task design and the worst outcome you can accept, not from expected load. For each dimension, ask what the largest legitimate single operation looks like for the intended task, then set the limit modestly above that and document the reasoning. The documented basis matters more than the number, because it tells reviewers what evidence would justify changing it.

Scope capability before you scope volume. NIST reports that many agent implementations limit write access by using tools with restricted interactions, or by constraining otherwise plausibly unlimited tools such as code execution. For a new agent, begin with read-oriented tools, restrict or exclude open-ended tools, and add write capability only when observed behavior supports it.

Apply least privilege and zero standing privilege alongside the numeric limits. NIST describes bounding access to the minimum scope needed for a designated task, with no persistent rights when the agent is idle. That scoping lets security systems contain or revoke an agent's authority if anomalous behavior or context drift is detected. Numeric thresholds are a second layer on top of this scoping, not a substitute for it.

Assign a tiered response to each threshold

Each threshold should map to a response before the agent goes live, with a named approver for anything that escalates. The response should match the severity of the breach and the cost of interrupting the agent.

• Allow: Activity within limits proceeds. Evidence of the applied policy is still recorded.
• Rate limit: Suited to volume breaches where the action is legitimate but the pace is not. It preserves reliability while slowing a possible loop.
• Escalate for human approval: Suited to ambiguous or higher-consequence actions, such as a first write operation or a tool call outside the expected pattern. It adds latency, so reserve it for cases where a human decision is needed.
• Block: Suited to actions that are never permitted for this agent. A block is predictable but can stop legitimate work if the rule is too broad.

Use escalation as a calibration mechanism

In the early period, set escalation thresholds tighter than you expect to keep them. Every escalation then produces a human decision with context: what the agent tried, what limit it reached, and whether the approver judged it legitimate. A pattern of approved escalations in one area is evidence that the limit can move. A pattern of rejected or suspicious ones is evidence that it should hold or tighten.

Decide the review criteria in advance. Examples include the share of escalations approved, how often rate limits are reached during normal work, and whether any block corresponds to a legitimate task. Set a review cadence that the policy owner commits to, and run an additional review after any change to the agent's tools, permissions or task scope. NIST AI 600-1 calls for evaluation before deployment and on an ongoing basis, and for go/no-go approval policies with minimum thresholds. Those thresholds concern performance and assurance criteria, not usage limits, but the principle of a documented gate followed by continuing review carries over.

Loosen one dimension at a time, and keep the previous value available for rollback. Changing several limits together makes it hard to tell which change altered behavior.

Enforce in the execution path and keep the record

A threshold that is only reviewed after the fact does not stop a loop. NIST describes technology components that trigger escalation to the correct authority, which implies enforcement separate from the agent itself. The agent should not be able to adjust, skip or reinterpret its own limits.

Trussed enforces policy in the execution path across models, agents, tools and MCP servers. Responses include allow, redact, block, rate limit and escalate for human approval, applied to individual interactions and tool calls. The policy is enforced at runtime, not reviewed after the fact. Enforcement generates continuous, audit-ready evidence of which policy applied and what the outcome was.

The threshold framework also needs its own change history. For each revision, record the limit and response in force, the time of the change, the approver, the rationale and the observed data that prompted it. NIST's AI RMF is a voluntary framework, as of October 2026, and its Playbook organizes work under Govern, Map, Measure and Manage. That structure is a reasonable way to assign ownership of threshold decisions. Pairing the change history with enforcement evidence lets a reviewer see both what the limits were and how they were applied.

• Limit and response in force for each dimension
• Time of each change
• Named approver for the change
• Documented rationale and the observed usage behind it
• Enforcement outcomes under each version of the policy