Concept
Agentic Workflow
A structured sequence of AI agent actions designed to complete a complex task - where the steps, tools, and decision points are defined in advance but the agent decides how to execute them.
Added May 18, 2026
The simplest use of an AI agent is a single turn: give it a task, it responds. But many real-world tasks require multiple steps, decisions based on intermediate results, iteration when something goes wrong, and access to different tools at different stages. An agentic workflow is the structure that organises these multi-step processes.
A workflow defines the high-level sequence of stages a task goes through and what kinds of actions are available at each stage. For a research workflow, the stages might be: gather sources, read and extract key information, synthesise findings, draft a report, review and revise. For a coding workflow: understand the requirement, design the solution, write the code, run tests, fix bugs, document. The workflow is the scaffolding; the agent fills in the specifics.
The distinction between agentic workflows and traditional software workflows is flexibility. A traditional workflow has rigid steps with predefined transitions. An agentic workflow allows the AI to make judgement calls at each step: how many sources to gather, which ones are worth reading fully, when the synthesis is complete enough, how to handle contradictory information. The workflow provides structure; the agent provides intelligence.
Reliable agentic workflows require careful design of checkpoints and error handling. Tasks that involve irreversible actions - sending emails, making purchases, deleting files - need human confirmation gates before execution. Long workflows need state persistence so they can be resumed if the session ends. Parallel branches need synchronisation points where outputs are collected and reconciled.
Dag-based (directed acyclic graph) workflow frameworks like LangGraph allow developers to define complex multi-step agentic workflows graphically, specifying the flow of information between steps and the conditions under which different paths are taken. These frameworks make it easier to build, test, and debug complex agentic behaviour than trying to manage the full state machine in prompt logic.
Analogy
A surgical procedure workflow in medicine. The sequence of steps is defined by the procedure type, but the surgeon makes real-time judgements about how to execute each step based on what they find. The workflow constrains and guides; the surgeon decides how to adapt. Agentic workflows apply this same structure-with-intelligence approach to AI tasks.
Real-world example
Sales development teams at software companies have deployed agentic workflows for prospect research: an agent receives a company name, automatically searches for recent news and company information, cross-references with the company's LinkedIn presence, extracts the relevant decision-makers, drafts a personalised outreach email, and queues it for human review. The workflow is defined; the agent handles the variable specifics of each company.
Why it matters
Agentic workflows are how AI moves from augmenting individual decisions to automating entire processes. The ability to string together multiple AI steps, each dependent on previous results, with error handling and human oversight at the right points, is what makes AI applicable to real business processes rather than just single-turn question answering.
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