Planning Loops, Termination, and Budgets
Explore how to design AI agent planning loops with clear termination policies and budget controls. Understand the differences between reactive and plan-driven execution, the role of orchestrators, and strategies to prevent infinite loops and cost overruns. Gain insights into observability and safe workflows for dependable agent system design.
Giving an agent tool access without rigid execution boundaries can be an operational hazard. An agent tasked with troubleshooting an invoice export failure can easily enter infinite loops, alternating between querying database logs and reading API documentation. Within minutes, the system exhausts its token quota, violates latency SLOs, and incurs uncontrolled inference costs without resolving the underlying task.
Unbounded execution is an orchestration failure rather than a prompt defect. Useful production agents operate inside an external control harness that governs when the system may advance, when it must stop, and what state persists across iterations. This lesson examines how to structure agent planning loops, contrast reactive execution against structured plan-then-execute models, enforce multi-tiered termination policies, and apply hard resource budgets across distributed agent workflows.
The agent loop
The loop is easiest to understand as an execution record managed outside the model. A request enters the orchestrator, which loads current state, tool outputs, and prior decisions. It then asks the model for the next action, executes that action, validates whether progress occurred, and records an updated state snapshot before deciding whether another step is allowed.
The orchestrator acts as the system of record for each run. It should track the following fields.
Step number: This counter gives every action an ordering boundary and supports max-step termination.
Observed state: This state includes user inputs, retrieved documents, tool outputs, open questions, and any compacted memory carried forward.
Chosen action: This action records whether the model responded directly, invoked a tool, requested clarification, or escalated.
Tool metadata: This metadata includes tool name, arguments, response payload, retry count, and execution status.
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