pipeline

To orchestrate multi-agent workflows with conditional branching, you route tasks to different agents based on conditions derived from previous agent outputs. This allows dynamic execution paths within your AI pipelines.

The best way to chain AI agents sequentially is to execute them in a defined order, passing data between each step. This enables sophisticated task automation outcomes that a single agent cannot achieve.

Yes, you can run multiple agents concurrently and merge their results. Parallel execution is supported natively, allowing you to handle complex multi-agent systems efficiently.

Multi-agent task chaining supports error handling and state persistence for complex AI-driven processes. This ensures your workflow automation remains robust and recoverable during execution.

You should use custom pipeline syntax for agent orchestration when you need to automate complex processes like code reviews, where agents must explore, analyze, and execute fixes sequentially or in parallel.

To pass data between agents in a branching workflow, the orchestration system manages data passing automatically, routing outputs from previous agents to conditionally inform the next execution step.