orchestrate-agent-workflow

To prevent infinite loops in multi-step LLM agent workflows, implement dual-layer termination combining semantic stop conditions with hard step caps. This structured orchestration framework enforces explicit termination conditions to ensure agent stability and stop runaway execution.

The best way to manage state and minimize context bloat in autonomous LLM agents is by using explicit scratchpads for state tracking. This maintains goal-relevance across multi-step tool-call orchestration by isolating task progress from the main context window.

Design verification gates for tool-call orchestration by enforcing per-step validation and human-in-the-loop gates for irreversible actions. This verification mechanism ensures agent reliability by intercepting critical decisions before execution during complex autonomous tasks.

Yes, this multi-step agent orchestration framework supports subagent decomposition for complex autonomous tasks. It applies planning and error recovery techniques to break down tasks, requiring strict adherence to step-based verification and budget monitoring throughout the process.

Use this orchestration framework for LLM automation tasks requiring complex workflows like autonomous research-to-PR pipelines. It is ideal when an agent must plan, execute, verify, and replan across multiple turns while avoiding task drift and maintaining stability.