Agentic Workflow
Also known as: agent workflow, LLM workflow, AI automation workflow, multi-step AI
An agentic workflow is a structured process in which an AI agent independently plans and executes a sequence of steps to complete a goal, using available tools and adapting its plan based on intermediate results.
Unlike a simple AI query (one input, one output), an agentic workflow involves the AI making decisions across multiple steps -- choosing which tools to call, evaluating the results, deciding what to do next, and knowing when the goal has been achieved. Agentic workflows are the operational pattern used to implement complex AI automation using large language models.
Agentic Workflow, explained at three levels
At home
An agentic workflow is when an AI handles a whole multi-step task on its own instead of waiting for you to tell it each step. If you ask a regular AI chatbot to 'research competitors and write a report', you would need to guide it through each step: first search, then read this, then write that. An agentic workflow lets you give the goal -- 'research competitors and write a report' -- and the AI plans and executes all the steps itself, using web search and writing tools, checking its own progress, and producing the finished report.
At work
Agentic workflows replace human coordination between multiple AI queries and tool calls. In a traditional process, a person might ask an AI to summarize a document, then use the summary to query a database, then format the results into a report. An agentic workflow automates the coordination: the agent performs each step in sequence, passing outputs from one step as inputs to the next, without the human having to manage the handoffs.
At home
The core loop in an agentic workflow is: perceive (observe the current state and any tool call results), reason (decide what to do next given the goal and current state), act (execute a tool call or generate a response), and update (record the result and loop). The agent continues this loop until the goal is achieved or a failure condition is detected. This pattern is sometimes called the ReAct loop (Reason + Act) or the agent loop.
At work
Agentic workflows can be structured in several patterns. Single-agent workflows have one agent performing all steps. Multi-agent workflows decompose the task across specialized agents (a planning agent, a research agent, a writing agent, a review agent) that communicate results and hand off sub-tasks. Hierarchical agent structures have an orchestrator agent directing worker agents. The right pattern depends on task complexity, available tool sets, and the reliability requirements of each step.
At home
Agentic workflows require careful design of failure and escalation behavior. An agent that encounters an unexpected result mid-workflow can fail silently (continue with incorrect information), fail loudly (stop and report an error), or escalate (pause and ask a human for guidance). Specifying the right behavior for each possible failure mode is as important as specifying the happy path -- particularly for workflows that interact with external systems or make irreversible changes.
At work
From a practitioner standpoint, the most common agentic workflow failure is under-specified success criteria. An agent given a vague goal ('research the competition') does not know when to stop, which sources are authoritative, or what level of detail is required -- and may loop indefinitely, produce inconsistent outputs, or stop prematurely. Effective agentic workflow design starts with a precise goal definition: what specific output should the agent produce, in what format, from which sources, with what quality threshold? The agent should be able to evaluate whether it has achieved the goal.
Real-world examples
At home
Fatima asked her AI assistant to 'find three plumbers in my area who are available this weekend, get their prices, and book the cheapest one for Saturday morning.' The agentic workflow searched local directories, called each plumber's booking API, compared prices, and confirmed a booking -- completing a task that would have taken Fatima 30 minutes of back-and-forth calls in about two minutes.
At work
A procurement team deployed an agentic workflow to process incoming vendor invoices. For each invoice, the agent: extracted the vendor name and amount, looked up the purchase order in the ERP, verified the amount matched within tolerance, routed discrepancies above £500 to a human reviewer, and posted matching invoices to the accounts payable system. The workflow handled 80% of invoices automatically, reducing the AP team's processing time by four hours per day.
How it compares
| Feature | Agentic Workflow | Traditional Automation (RPA) | Single LLM Query |
|---|---|---|---|
| Handles variation | Yes -- reasons and adapts to unexpected results | No -- breaks when inputs deviate from expected format | N/A -- single response, no steps |
| Multi-step | Yes -- plans and executes sequences of tool calls | Yes -- scripts execute fixed step sequences | No -- single input, single output |
| Natural language input | Yes -- goal specified in natural language | No -- requires structured configuration | Yes |
| Error handling | Can reason about errors and adapt or escalate | Typically fails or escalates rigidly on error | N/A |
| Auditability | Requires explicit logging; each step must be captured | Full step log typically built-in | Single input/output logged |
Agentic workflows can handle variation and unstructured input that breaks rigid RPA scripts, but require more careful design of failure handling and oversight. Single LLM queries are simpler but cannot automate multi-step processes.
For analysts
In BA artefacts, agentic workflow requirements need to specify the happy path, failure paths, escalation triggers, and human oversight points in equal detail. Use a process model (BPMN or similar) to map the workflow before writing requirements -- this makes the agent's decision points, tool calls, and escalation conditions visible. Specify at minimum: the goal definition the agent receives, the tools available to it, the human approval gates for irreversible actions, the logging requirements, and the fallback behavior when the agent cannot complete a step. These workflows often require an initial pilot on a representative sample before committing to full automation.
History & origin
The term agentic workflow gained currency alongside the emergence of LLM-based agents from 2023. Frameworks for building agentic workflows (LangChain, LangGraph, Autogen, CrewAI) standardized common patterns: the ReAct loop (Reason + Act), the Plan-and-Execute pattern, and multi-agent orchestration. Andrew Ng's 2024 framing of agentic AI as one of the most impactful patterns in applied AI helped crystallize the concept for practitioners. Enterprise adoption accelerated through 2024-2025 as organizations built production agentic workflows for document processing, research, code review, and customer service automation.