Agentic Workflows

Traditional automation follows fixed rules. If a form arrives with these fields, send it here; if the amount is above a threshold, route it for approval. This works well for predictable work but breaks down when inputs vary, information is incomplete or judgment is needed. Agentic workflows combine the structure of a defined process with the flexibility of AI agents. The workflow sets out the overall steps, checkpoints and rules, while agents handle the parts that require reading, interpreting, deciding and acting, such as understanding a customer's request, finding relevant information or drafting a response.

The term became common in 2024, when practitioners such as Andrew Ng argued that wrapping language models in iterative workflows, where they plan, use tools, review their work and try again, often produced better results than single prompts to more powerful models. In business settings, agentic workflows are now used for claims handling, customer onboarding, procurement, IT service management, financial close, sales operations and many other processes. They sit between fully rule-based automation and fully autonomous agents, giving organizations a way to apply AI to real work while keeping control over how it flows. For operations leaders, process owners, automation teams, AI engineers and risk teams, agentic workflows are one of the most practical ways to put AI to work.

Definition of Agentic Workflows

Agentic workflows are structured, multi-step processes in which one or more AI agents perform tasks such as interpreting inputs, retrieving information, reasoning, calling tools and producing outputs, while the workflow defines the sequence, decision points, permissions, human checkpoints and exception paths that keep the work controlled and auditable.

They are a practical form of agentic AI, extend workflow automation and are coordinated through AI orchestration.

Elements of an Agentic Workflow

Trigger. An event that starts the workflow, such as a new request, document or record change.

Agent steps. Tasks performed by AI agents, such as classifying, summarizing, researching, drafting or deciding within limits.

Deterministic steps. Rule-based actions, such as validations, calculations or system updates, that do not need AI.

Human checkpoints. Points where people review, approve or handle exceptions.

Tools and data. The systems, APIs and knowledge sources agents use, along with logs that record what happened at each step.

Agentic Workflows vs. Other Automation

Rule-based automation. Fixed logic handles predictable inputs well but fails when inputs vary or judgment is needed.

Single AI calls. One prompt to a model produces an output, but without the ability to gather information, check work or take further steps.

Agentic workflows. Defined process structure combined with agent steps that handle variation and judgment.

Fully autonomous agents. Agents decide the whole sequence themselves, offering flexibility but less predictability and control.

Importance of Agentic Workflows in Enterprise Operations

They apply AI to real processes. Work that involves documents, messages and judgment can be automated more fully than with rules alone. Cases that once required a person to read every document can be prepared or completed automatically.

They balance flexibility and control. The workflow structure keeps processes predictable and auditable while agents handle variation.

They also improve over time. Logs of agent decisions and human corrections show where to refine instructions, tools and rules.

Agentic Workflows and Financial Management

Agentic workflows can reduce the cost of processing each case by handling routine work and preparing complex cases for people.

Model usage costs vary by step, so designers can use smaller, cheaper models for simple steps and more capable models only where needed.

Measuring cost and time per case before and after automation shows the financial impact.

Implementation requires investment in integration, testing and monitoring, which should be included in business cases. Ongoing costs for monitoring and refining agents should be included as well.

Common Challenges

Workflows are designed around old processes, adding agents without removing unnecessary steps. The result is an old process that runs slightly faster rather than a better process.

Agent steps are given too much latitude, producing inconsistent results. Narrow instructions and specific tools for each step usually work better than broad, open-ended tasks.

Exceptions are not handled well, so cases get stuck or are processed incorrectly. Every workflow needs a clear path for cases the agents cannot handle confidently.

Monitoring is limited, making it hard to see where agents perform poorly or costs rise. Without step-level data, teams cannot tell which part of the workflow to improve.