What are agentic workflows? A plain-English guide

An agentic workflow is a process where an AI model does more than answer once: it works through a task in steps, decides what to do next and uses tools along the way. IBM defines them as AI-driven processes where autonomous AI agents make decisions, take actions and coordinate tasks with minimal human intervention.

The word “agentic” means the AI has some agency: room to choose. In a normal automation, a person writes every rule in advance (“if the form says X, send email Y”). In an agentic workflow, the person sets the goal and the guardrails, and the AI picks the steps within them.

Anthropic draws a useful line inside this idea. In its guide Building effective agents (December 2024), it calls systems where models and tools follow predefined code paths “workflows,” and systems where the model directs its own process and tool use “agents.” Most real products mix the two: a fixed outline with AI deciding inside each step.

AI agentic workflows run a loop: plan, act with a tool, check the result, and repeat until the goal is met or a person needs to step in. In a March 2024 letter, AI researcher Andrew Ng described four design patterns behind most of them:

• Reflection: the AI reviews its own output and finds ways to improve it.
• Tool use: the AI calls outside tools, such as web search, a calculator, a database or code.
• Planning: the AI breaks a goal into a multi-step plan and carries it out.
• Multi-agent collaboration: several AI agents with different roles split the work and check each other.

The loop is what makes the difference. Ng reported that on HumanEval, a coding test, GPT-3.5 answering in one go scored 48.1% and GPT-4 scored 67.0%, while GPT-3.5 wrapped in an agent loop reached up to 95.1%. The same model did far better when it could draft, test and fix its work.

1. 01A trigger starts the work: a new support email, a failed payment, a schedule.
2. 02The AI reads the context and plans what to do.
3. 03It uses a tool: looks up the customer, checks an order, drafts a reply.
4. 04It checks the result against the goal and its rules.
5. 05It finishes, tries another way, or hands over to a person for approval.

Anthropic’s guide lists five workflow patterns that cover most real setups. Each one adds a little more freedom for the AI.

Traditional automation is better when the steps never change; agentic workflows are better when the right next step depends on what just happened. IBM notes that rule-based automation such as RPA follows predefined rules, which suits repetitive, standard tasks, while agentic workflows adapt to real-time data and unexpected conditions.

If you are mixing up “agent” and “agentic AI” as well, our guide to AI agents vs agentic AI separates the terms.

The best examples are everyday business jobs that need a little judgment at each step. IBM gives an IT support example: a rule-based chatbot follows a fixed decision tree and escalates, while an agentic assistant asks clarifying questions, runs diagnostics, retries a different approach and escalates with a summary of what it tried.

• Customer support: read the message, look up the customer’s plan and history, answer, and hand over to a person when a refund falls outside the rules.
• Failed payments: spot the failed charges, group the customers, draft a reminder, and wait for the owner to approve before sending.
• Content: draft a blog post from a brief, check it against the brand rules, fix it, and queue it for review.
• Lead follow-up: read a new sign-up, research the company, draft a personal note and suggest when to send it.
• Reporting: pull last week’s numbers, explain what changed and suggest one test to run next.

Notice the pattern: the AI does the reading, looking up and drafting, and a person approves anything that sends, charges or publishes. That split is where most small businesses get the benefit without the risk.

Use one when the task has several steps and the right step depends on the situation; skip it when a single AI call or a fixed rule does the job. Anthropic’s advice is to find the simplest solution possible and only add complexity when needed, because agentic systems often trade latency and cost for better task performance.

• Cost: every plan, check and retry is another paid AI call, so loops add up fast.
• Errors compound: a wrong fact in step one can carry through every later step.
• Speed: loops take longer than one answer, which matters in live chat.
• Control: decide up front what the AI may do alone and what always needs your OK.

Start from one job, not from a framework. Developers use code libraries; non-developers can use automation tools or an AI app builder that sets up the agents for them.

1. 01Pick one repeated job that eats your time, such as answering “where is my order?” emails.
2. 02Write down the steps a good employee follows, and where they use judgment.
3. 03List the tools it needs: your customer list, orders, help articles, email.
4. 04Mark the actions that need your approval: refunds, sends, anything public.
5. 05Run it on real examples, read every output for a week, then loosen the rules where it earned trust.

For the agent side in more depth, see how to build an AI app. If you want to sell this as a service to other businesses, read how to start an AI automation agency.