Agentic workflow
An agentic workflow is a multi-step business process orchestrated by AI agents — where one or more LLM-driven agents make decisions, call tools, and adapt to inputs rather than following a fixed automation script.
Agentic workflows replace traditional fixed-script automation (Zapier-style triggers and actions) with adaptive LLM-driven decision-making. Common 2026 examples: customer support triage + response, sales lead enrichment + outreach, code review + ticket routing, content brief + draft + publish, RFP intake + drafting. Frameworks: LangGraph, n8n AI nodes, OpenAI Agents SDK, Mastra. The trade-off: agentic workflows are more flexible than fixed automation but harder to debug, monitor, and prove correct. Best practice in 2026 is to add explicit guardrails, evals, and human-in-the-loop checkpoints around the agentic core.
When to use agentic workflow
- Workflows where input variability makes fixed scripts brittle.
- Multi-step business processes that benefit from adaptive routing.
Common mistakes
- Replacing reliable fixed automation with brittle agentic workflows.
- Skipping evals and observability — agentic workflows fail silently.
FAQ
What is agentic workflow?
An agentic workflow is a multi-step business process orchestrated by AI agents — where one or more LLM-driven agents make decisions, call tools, and adapt to inputs rather than following a fixed automation script.
When should I use agentic workflow?
Workflows where input variability makes fixed scripts brittle. Multi-step business processes that benefit from adaptive routing.
What are the most common mistakes with agentic workflow?
Replacing reliable fixed automation with brittle agentic workflows. Skipping evals and observability — agentic workflows fail silently.
Related terms
- AI agent — An AI agent is a system where a language model autonomously plans and executes a sequence of tool calls to accomplish a goal.
- Agent loop — An agent loop is the repeating cycle of an AI agent — observe state, decide on an action (usually a tool call), execute, observe the result, and repeat — until a goal is reached or a stop condition fires.
- Evals (LLM evaluations) — Evals are systematic tests that measure how well a language model or LLM-powered system performs on a defined task using a golden set of inputs and reference outputs.
- Multi-agent system — A multi-agent system is a coordinated set of specialised AI agents that delegate to each other — each agent has a focused role, tool set, and system prompt rather than one mega-agent doing everything.
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/agentic-workflow.md.