Agents on Conductor — Architecture for Production AI

Conductor has introduced first-class support for running AI agents as durable, policy-enforced workflows, arguing that production agents require a separation between an LLM reasoning plane and a deterministic execution plane. The platform compiles agents built with its SDKs or frameworks such as LangChain, LangGraph, OpenAI, Vercel, and Google ADK into workflows where model tool calls, plans, guardrails, schemas, credentials, and approval requirements are validated before execution. Unsupported tools and invalid multi-step plans are rejected rather than partially run, while tools can include worker functions, APIs, MCP tools, people, other agents, or full workflows. Because agent state and each workflow transition are persisted, executions can survive crashes, deployments, long waits, approvals, and external events, with built-in retries, replay, scheduling, and messaging. The release promotes designing narrowly scoped agents with limited tools and credentials, then composing them with deterministic workflows for tasks such as infrastructure changes, combining LLM judgment with repeatable operational procedures. Conductor presents determinism, durability, and explainability through recorded execution histories as the core properties needed to operate agents in production.

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