AI Agent Budget Allocation Across Sub-Agents: Allocating and Enforcing Limits at Runtime

Why a shared budget breaks when agents delegate

A single agent with a single session cap is straightforward to reason about. Delegation changes that. A parent hands work to sub-agents, those sub-agents may run in parallel, retry failed steps, call tools the parent never anticipated, and in some designs spawn agents of their own. Unless the split is explicit, the same spend can be counted twice, a single branch can consume most of the allowance, or the total can be exceeded because each agent only sees its own counter.

OWASP guidance documents the underlying risk as resource exhaustion through uncontrolled tool chaining. An agent loops or chains external calls with no loop-termination guard and no per-session resource budget, and each tool call can trigger further downstream services. OWASP recommends hard caps per agent session on tool calls, API requests, and tokens, with the session terminated when the cap is reached. Those caps are the starting point. The design question for multi-agent systems is how to derive them for each sub-agent from one parent allowance.

Budget splitting is therefore a policy design and runtime enforcement problem. It is about bounding what an agent is permitted to do, not about optimizing spend.

Four allocation models compared

These models are design patterns, not framework features, and they can be combined. The right choice depends mainly on how predictable each sub-agent's workload is and how much a single branch is allowed to affect the others.

• Fixed partition: The parent assigns each sub-agent a set slice up front. Simple to reason about and prevents one branch from starving another. Unused allowance is stranded, and a slice that is too small stops useful work. Suits predictable, repeatable tasks.
• Proportional share: Slices are sized by weight, such as expected task complexity. Better fit for uneven work than equal slices. The weights are estimates, so they need review, and the unused remainder is still stranded.
• Shared pool with per-agent ceilings: All agents draw from one pool, and each has a hard maximum. Flexible when demand varies. Under parallel fan-out the pool can empty before late agents start, so ceilings alone do not guarantee fairness.
• Reserve and release: An agent reserves its allowance before delegating or spawning and releases the unused portion on completion. Prevents double counting and bounds fan-out and nested delegation, because a child can only draw from what the parent reserved. Requires reliable release on failure and timeout.

Choosing a model and defining the limit dimensions

Use fixed partition or proportional share when tasks are well understood and the main concern is isolation between branches. Use a shared pool with ceilings when demand is bursty and sub-agents are interchangeable. Use reserve and release when delegation is dynamic, when agents can spawn further agents, or when parallel fan-out is common. In that case, require that a child's budget is carved out of its parent's, so total consumption cannot exceed the root allowance regardless of depth.

The limit dimensions named in OWASP guidance are tool calls, API requests, tokens, and session duration, with per-user and per-tenant rate limits applied independently of agent-level limits so that one session does not affect others. Add model calls as a separate counter if your workload distinguishes them from token volume. Retries should count against the same budget as the original attempt, otherwise a retry storm consumes allowance that no one accounted for. Scope each sub-agent's tool access to its task, in line with least-privilege agency, so a limit on call counts is not the only thing bounding what a sub-agent can reach.

Caps on an agent's own calls may not bound total downstream impact, since each tool call can cascade into other services. Decide deliberately which side of that boundary each limit is enforced on.

Deciding what happens at exhaustion

Exhaustion behavior is part of the policy and should be defined and tested before deployment. The documented baseline is to terminate the session at the cap, or raise a rate-limit exception. Regulated workloads usually need more than one outcome, chosen by dimension and by agent.

• Block: Stop the call or terminate the session. Appropriate for hard caps on tool calls and for any action with external side effects.
• Rate limit: Slow or queue calls once a rate threshold is reached. Appropriate for per-user and per-tenant limits where work can safely resume.
• Escalate for human approval: Pause and ask a person to approve additional allowance or a specific action. Appropriate where continuing carries consequence.
• Allow with a record: Permit the call but record that a threshold was crossed. Use sparingly and only for low-risk dimensions.
• Return unused allowance: On completion or failure, release the remainder to the parent so it is not stranded.

Why enforcement belongs in the execution path

A limit defined in application code or configuration is advisory unless something stops the call when the limit is reached. The agent, or a sub-agent it invokes, can fail to check it, misread it, or be manipulated into ignoring it. OWASP guidance advises enforcing authorization in the execution component, outside the agent's context, so that limits are not something the agent can read, ignore, or alter. Applying that principle to budgets means the counters, ceilings, and exhaustion behavior live where model calls and tool calls actually pass.

Enforcement at that layer also produces the evidence a regulated enterprise needs. If every interaction and tool call is evaluated against a policy at the moment it occurs, each decision can be recorded with the limit that applied, the agent it applied to, and the outcome. That is a record of what an agent was permitted to do and what happened when a limit was reached, generated by the control itself rather than reconstructed afterward.

Trussed enforces policy in the execution path across models, agents, tools, and MCP servers, with outcomes that include allow, redact, block, rate limit, and escalate for human approval. Enforcement is applied at runtime, not reviewed after the fact, and generates audit-ready evidence that is mapped to HIPAA, NAIC, NYDFS, the EU AI Act, and NIST. The scope is individual interactions and tool calls. It is not transaction-level governance across tasks and agents, and it is not end-to-end tracing of agentic workflows. A per-call enforcement point is one input to those larger questions, not a replacement for them.