Defining Inference-Time Compute Governance
Inference-time compute governance refers to the runtime controls that constrain how much computation, reasoning depth, or tool-call activity an AI agent consumes while completing a task. Unlike governance applied during model training or fine-tuning, these controls operate at the point where a request is executing, as a reasoning-capable model iterates through internal reasoning steps, calls external tools, or generates output tokens. The category has emerged because reasoning models and multi-step agents now expose configurable parameters that directly affect cost, latency, and the scope of autonomous decision-making, but no single vendor or standards body defines a unified control plane for enforcing these limits across a fleet of agents. Enterprises currently assemble the practice from model-provider API parameters, orchestration or gateway layers, and general AI risk-management frameworks such as NIST's AI Risk Management Framework, which provides governance structure but not technical specification for reasoning-step or token-budget enforcement.
How Reasoning Budgets Are Set at the Model Layer
Reasoning-capable models expose their own compute controls at the API level. OpenAI's o-series models accept a reasoning_effort parameter, set to values such as low, medium, or high, that constrains how much internal reasoning computation the model performs before returning a response. Anthropic's Claude extended-thinking mode uses a budget_tokens parameter to cap the number of tokens the model can allocate to internal reasoning before producing a final answer. Both providers also allow a separate maximum output or completion token limit, independent of the reasoning-effort or thinking-budget setting, meaning a single request is often governed by more than one limit configured separately. These parameters are set per request or per task, and there is no evidence of a persistent, centrally enforced default across sessions unless an application layer applies one. This matters for cost visibility because OpenAI documentation notes that reasoning tokens generated by o-series models are billed as output tokens even though they are not returned to the user, which ties reasoning depth directly to cost in a way that is not visible in final response length.
Risks of Operating Without Compute Governance
The absence of inference-time compute governance is increasingly recognized as a security gap rather than only a cost concern. The OWASP Top 10 for LLM Applications, 2025 edition, includes Unbounded Consumption as a named risk category, covering resource exhaustion and excessive operational cost that result from uncontrolled model or agent resource use. OWASP's guidance identifies denial-of-wallet and denial-of-service scenarios that can arise from unmetered inference requests, including multi-step agentic workflows where a single task can trigger many reasoning cycles or tool calls without an upper bound. In agentic systems this risk compounds, because each additional reasoning step or tool call can itself trigger further steps, and without a ceiling on iterations a single malformed or adversarial task can consume disproportionate compute, extend latency, or execute an unintended sequence of actions that falls outside existing monitoring.
Coordinating Multiple Limit Types
Reasoning-effort settings, token budgets, maximum output limits, and tool-call ceilings are configured independently of one another, and each governs a different dimension of inference-time cost. A model can be set to a high reasoning-effort tier while an agent framework simultaneously caps tool calls at a low number, producing behavior inconsistent with the intended task scope. Because these settings are vendor-specific, meaning Anthropic's token-budget approach and OpenAI's tiered effort setting are not directly equivalent, cross-model governance requires enterprises to normalize policy definitions rather than assume a single limit type applies uniformly. Coordinating these limits is a configuration and policy-design exercise, not a default platform behavior, and it requires the orchestration layer to reconcile settings across providers.