Agentic Workflows

An agentic workflow is a multi-step business process where AI agents carry out the steps : reading, checking, writing, notifying : along a predefined path, under explicit rules, with a human approving the points that matter. The structure makes it reliable; the agents inside the steps make it adaptable.

The previous page explained what a single AI agent is. Real business processes, however, are rarely a single task : they are chains of steps. An invoice arrives, it must be read, checked against the order, recorded, and someone must be notified if something does not match. An agentic workflow is the way we structure AI to handle such multi-step processes reliably.

A useful analogy : one agent is a talented employee. An agentic workflow is a well-organized team with a procedure : each member has a clear role, work moves from desk to desk in a defined order, quality is checked along the way, and a human signs off where it matters.

Workflows vs agents : an important distinction

In its reference article Building Effective Agents, Anthropic draws a line that practitioners find very useful :

• A workflow orchestrates the model and its tools through predefined paths : you decide in advance which steps happen in which order. Predictable, testable, governable.
• An agent decides dynamically how to reach the goal : it chooses its own steps and tools as it goes. More flexible, less predictable.

The best systems often combine both : a predefined workflow for the parts of the process that must be reliable and auditable, with agent autonomy inside individual steps where flexibility pays off. Anthropic's core finding is worth quoting : the most successful implementations use simple, composable patterns : not complex frameworks.

The five workflow patterns

Anthropic describes five recurring patterns. You do not need to memorize them : recognize them, because every real agentic system is built from these bricks.

1. Prompt chaining : break the task into a fixed sequence of steps, each step handling the output of the previous one. Example : extract the data → check it → format the report.
2. Routing : a first step classifies the input and sends it down the right branch. Example : an incoming email goes to the « invoice » path, the « complaint » path or the « other » path.
3. Parallelization : several calls work at the same time on different aspects, and the results are combined. Example : eight specialists each audit one aspect of a website simultaneously.
4. Orchestrator-workers : a central agent breaks the task down, delegates the pieces to worker agents, and assembles the results. Suited to tasks where the sub-steps cannot be known in advance.
5. Evaluator-optimizer : one agent produces, another evaluates and demands corrections, in a loop, until quality is reached. This is how you encode « review before delivery » into the system itself.

RPA, agentic workflow or autonomous agent : which one do you need?

The three terms are often mixed up. The differences decide budget, risk and maintenance :

	Classic automation ( RPA / scripts )	Agentic workflow	Autonomous agent
Path	Fixed script, written in advance	Predefined steps, AI inside each step	The AI chooses its own steps
Handles variation	Breaks ( renamed column, odd email )	Adapts within each step	Adapts everywhere
Predictability	Total	High : the chain is known	Lower : behaviour emerges
Governance	Trivial	Explicit gates and permissions	Hard : requires strong guardrails
Best for	Stable, high-volume, never-changing tasks	Multi-step business processes with variation	Open-ended tasks ( research, diagnosis )
Typical failure	Silent breakage after any change	Badly placed approval gate	Confidently doing the wrong thing

Rule of thumb : if you can write the steps down, build a workflow and put agents inside the steps. Reserve full autonomy for bounded, low-stakes explorations : and wrap it in the guardrails below.

What makes an agentic workflow reliable

Connecting AI steps is easy ; making the chain trustworthy is the real work. Four mechanisms do most of it :

• Guardrails at each step : explicit permissions ( allowed alone / needs approval / forbidden ), so autonomy never exceeds what the process owner decided.
• Human validation at key points : the workflow pauses where stakes are high : sending an email, paying an invoice, publishing : and a person approves. The goal is not to remove humans, it is to spend human attention exactly where it matters.
• Verification before « done » : each step proves its result ( the test passed, the file exists, the number matches ) instead of declaring success.
• Memory and lessons : the system records its mistakes and the rules learned from them, so the same error does not happen twice.
• A way to see it still works : beyond each step proving its own result, someone ( or a daily summary ) watches the whole chain over time, so you notice when quality drifts before a customer does.

These mechanisms are not theory : they are configuration. Our Claude Code guide shows how each one maps to a concrete file or setting you can copy.

How a step actually does anything : tools. A reasoning model on its own can only write text. What lets a step read your CRM, query a database or send an email is a tool the agent is allowed to call : the same building block covered on the AI agents page. A recent standard, the Model Context Protocol ( MCP ), is simply a common plug so an agent connects to your systems the same way every time : think USB-C rather than a different cable per device. The rule stays the same : a tool that only reads is low-risk ; a tool that acts ( sends, pays, publishes ) sits behind an approval gate.

Real examples, with numbers

Abstract patterns become convincing when you see what they cost and produce. Three workflows we run in production at eaQbe or operate for clients :

• Pipeline health check ( routing + chaining + evaluator ) : every morning, an agent checks the health of a data pipeline. If something failed, it investigates the logs ( locate → diagnose → propose ), fixes what is within its permissions, and writes a report. Anything outside its mandate is escalated to a human with the diagnosis already done. Quality gates check its conclusions against the database before the report goes out.
• Weekly SEO/GEO audit ( parallelization + orchestrator ) : specialized agents each audit one dimension of a website ( indexing, speed, structured data, AI citability… ) and an orchestrator assembles a ranked report with recommendations. Model cost per run : between one and two euros. A consultant doing the same sweep manually needs half a day.
• Read-only file-server audit ( orchestrator-workers + verification ) : before a document-management migration, six specialized agents inventoried 826,000 files ( 3.2 TB ) : duplicates, obsolescence, permissions, content sampling : without modifying a single file. The entire run was re-executed five days later and reproduced at less than 0.2% divergence, which is what made the numbers defensible in front of the client's management.
• Bulk operations under API quotas ( chaining + guardrails ) : tagging 5,000 member records through a SaaS API capped at 20 requests per minute. The workflow paces itself, backs off on errors, resumes where it stopped after any interruption, and logs every call. Human effort : reading the morning report.

The pattern behind all four : structure decides reliability, agents provide the intelligence, humans keep the judgment.

When NOT to build an agentic workflow

Being honest about the limits is part of the method :

• The task never varies : a fixed export that runs identically every night needs a script, not a reasoning model : cheaper, faster, deterministic, and with none of the compounding errors a multi-step agent can accumulate.
• Nobody can state the rules : if the process owner cannot say what « correct » looks like or where approval matters, the workflow will encode confusion. Clarify the process first, automate second.
• Errors are irreversible and ungateable : if a wrong action cannot be caught by a human checkpoint before it causes harm ( an outbound payment, a legal filing ), keep a person in the execution seat, and use AI only to prepare the work.

A reality check worth knowing. Gartner expects more than 40% of agentic-AI projects to be canceled by the end of 2027 : mostly from escalating cost, unclear business value and weak risk controls, and it warns of « agent washing » ( ordinary RPA or chatbots relabelled as agents ). None of that argues against agentic workflows : it argues for the discipline on this page : pick one process you can state clearly, put the guardrails in first, and prove the value before you scale. [Source : Gartner, June 2025.]

How to start

The reliable route we teach and apply : pick one recurring multi-step process that costs a few hours per week, write down its steps and its approval points, build it with the guardrails above, and run it in shadow mode ( it produces, a human still executes ) for two weeks before letting it act. One working workflow teaches a team more than any slide deck : it is exactly what we build together in our Claude Code & Agentic AI training.

Frequently asked questions

What is an agentic workflow in simple terms?

A multi-step business process where AI agents execute the steps : reading, checking, writing, notifying : under explicit rules, with human approval at the points you choose. Predefined structure for reliability, agent intelligence inside the steps.

What is the difference between an agentic workflow and automation ( RPA )?

Classical automation follows rigid scripts and breaks when reality deviates ( a renamed column, an unusual email ). Agentic workflows put a reasoning model inside each step, so the system adapts to variations and explains what it did : while keeping the overall process structured.

Do agentic workflows replace people?

They replace the repetitive execution, not the judgment. Well-designed workflows route exactly the right decisions to humans : with the context already assembled : and handle the rest. The human role shifts from doing every step to setting the rules and approving what matters.

What are real examples of agentic workflows in business?

Recurring examples from production : a weekly SEO audit where specialized agents each check one dimension of a website and a report is assembled for under two euros of model cost ; a read-only file-server audit where six agents inventoried 826,000 files and the run was reproduced at less than 0.2% divergence ; a member-care assistant that drafts replies from company data while a human validates every send.

How much does an agentic workflow cost to run?

Far less than most teams expect. The dominant costs are model tokens and engineering time, not infrastructure : a weekly multi-agent website audit costs one to two euros per run in tokens ; a nightly bulk operation on 5,000 records costs cents. The expensive part is designing the guardrails once ; running the workflow is cheap.

When should you NOT use an agentic workflow?

When the task is fixed, high-volume and never deviates ( classic scripts are cheaper and faster ) ; when no one can state the rules and approval points ( automate a process you understand, not one you hope the AI will figure out ) ; and when errors are irreversible and cannot be gated by a human checkpoint.

Going further

• See what an AI agent is if you have not read it yet.
• Learn the tool we use to build these workflows : the Claude Code guide.
• Build one yourself, guided : the Claude Code & Agentic AI training ( 1 day, Brussels or remote ).