trigger-agents

You can orchestrate parallel workers in an LLM workflow by applying orchestrator-workers and parallelization patterns to delegate tasks concurrently. This approach allows a central agent to dynamically assign subtasks to worker agents for faster execution and aggregated results.

Add human-in-the-loop approvals to an AI agent workflow by implementing production workflow controls that pause task execution. This pattern ensures durable multi-step executions wait for manual validation before publishing findings or proceeding to the next phase.

Common routing and prompt chaining patterns for AI agents include dynamic task distribution and sequential prompt execution. These orchestration patterns allow an agent system to direct inputs to specialized sub-agents and refine outputs through chained steps.

Yes, Trigger.dev supports durable multi-step AI agent executions through its workflow primitives. You can use these primitives to build reliable AI automation systems that handle tool calling, realtime streams, and error handling without losing state.

You need an evaluator-optimizer loop for LLM-powered applications when complex multi-step tasks require validation against specific criteria. This pattern evaluates agent outputs and iteratively refines the generation process to improve accuracy and reliability.

The best way to handle tool calling and error handling in multi-agent systems is using durable workflow primitives that coordinate task execution. This approach provides reliable AI automation by managing external tool integrations and recovering from execution failures.