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Agentic Workflows

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.

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.

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.

A concrete example​

Here is a real workflow pattern we operate at eaQbe : every morning, an agent checks the health of a data pipeline ( routing : healthy or not? ). If something failed, it investigates the logs ( prompt chaining : 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 ( evaluator ) check its conclusions against the database before the report goes out.

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.

Going further​

Put this into practiceeaQbe, the team behind these tutorials, delivers hands-on trainings and consulting in Brussels and remotely.