Introducing the AGENT task: Calling AI Agents from Your Conductor Workflows

Conductor’s AGENT task integrates AI agents into durable workflows, allowing them to operate alongside HTTP calls, human approvals, parallel branches, and other task types while gaining workflow features such as retries, timeouts, state persistence, observability, and error handling. It supports external A2A-compatible agents through the default `a2a` mode, which can use a direct endpoint or agent-card URL, and agents deployed on Conductor through `conductor` mode, including agents built with supported SDKs or imported from frameworks such as OpenAI Agents, LangGraph, and Google ADK. When an agent does not return immediately, Conductor records its remote task identifier and repeatedly polls for completion; completed results provide response text, state, task and context identifiers, and, for Conductor-hosted agents, a child-workflow execution ID. These identifiers enable multi-turn conversations and human-in-the-loop flows when an agent requests more input, while workflow branching can respond to states such as completed, failed, canceled, or input-required. Configurable duration and polling-failure limits prevent workflows from waiting indefinitely and let standard workflow error handling manage unavailable or slow agents.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.