Bounded AI Agent Execution

An agent loop is a control plane. Every tool call should be authorized and every run should terminate within explicit economic and operational limits.

AI infrastructure · TypeScript

Bounded Agent Execution

A model proposes actions; deterministic policy owns authority, limits, and termination.

const budget = { steps: 12, tokens: 24_000, toolMs: 30_000, spendUsd: 0.40 }
for (const step of plan) {
  const reservation = usage.reserve(step.maximumUsage, budget)
  authorize({ actor, tool: step.tool, resource: step.resource })
  if (isIrreversible(step)) await requireApproval(runId, step)
  const result = await execute(step, { timeoutMs: reservation.toolMs })
  usage.settle(reservation, result.usage)
  audit.append({ runId, step, result: redact(result) })
}

Invariant: No step starts without reserved budget and authorization for its exact tool and resource.

Use when: An agent can call tools and needs explicit economic and safety limits.

Why this boundary matters

Agent loops can compound tool calls, cost, and irreversible effects. Authority and resource ceilings must remain outside model control.

Failure policy

BoundaryAction
Within all budgets and authorizedExecute the next tool step and audit it
Step, token, time, or spend budget exhaustedStop with an explicit terminal outcome
Tool or resource unauthorizedDeny without asking the model to override policy
Irreversible or high-impact actionRequire deterministic approval
Tool outcome is ambiguousReconcile by stable operation ID before retrying

Trade-offs

Tight budgets contain cost and blast radius but can truncate useful work. Broad tool access improves autonomy while increasing security and recovery burden. Human approval adds latency but creates a deliberate boundary for irreversible effects.

Decision rule: Grant the minimum authority and budget needed for one run, and require deterministic controls around consequential actions.

Further reference

Browse all engineering snippets · Read about agent control planes

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