How to Build an Agentic Workflow: Patterns, Steps, and Limits

The short answer

An agentic workflow is a goal-driven process where an AI agent plans a sequence of steps, acts on them through tools, observes what comes back, and adapts until it reaches the goal, with minimal human intervention. A deterministic workflow follows a fixed script; an agentic one decides its next move at runtime based on what it sees. That makes agentic workflows suited to ambiguous, variable tasks where the right path is not known in advance. The cost is that every run re-reasons, which is where the Major take below comes in: reason once, then compile the repeatable steps into an app.

Key takeaways • An agentic workflow plans, acts, observes, and adapts toward a goal, rather than following a fixed script. • Use agentic workflows for ambiguous, variable tasks; use deterministic automation for stable, high-volume, auditable steps. • The highest-value move is to reason once, then compile the repeatable steps into a governed app.

What an agentic workflow actually is

The word that matters is goal. You give the agent an objective, not a script, and it works out the steps. It reasons about what to do, calls tools or APIs to do it, reads the results, and revises its plan. The same request can take different paths on different runs, because the agent responds to what it finds rather than to a predetermined branch. That adaptivity is the point, and it is also the source of the cost and governance questions later in this article. To go deeper on the actor at the center of it, see what an AI agent is.

The plan-act-observe loop

Most agentic workflows run the same underlying loop:

1. Plan. The agent breaks the goal into steps and decides what to do first.
2. Act. It calls a tool, queries a system, or runs code to carry out the step.
3. Observe. It reads the result, checks progress against the goal, and either continues, revises the plan, or stops for a human.

Core components: reasoning, tools, memory, orchestration

Four building blocks show up in almost every agentic workflow. Reasoning is the model deciding what to do next. Tools are the functions and APIs the agent can call to act on the world. Memory is the state it carries across steps and runs, from a scratchpad to a database. Orchestration is the control logic that runs the loop and coordinates multiple agents when a job is split up. The common patterns are built from these four: planning to sequence the work, tool use to act, reflection to critique and retry its own output, and multi-agent collaboration to divide a task across specialized agents.

What a deterministic (traditional) workflow actually is

A deterministic, or traditional, workflow is rule-based automation. A trigger fires, a fixed sequence of steps runs, and the same input always produces the same output. This is what a Zapier Zap, a cron job, or an RPA script does. There is no reasoning at runtime; the logic was decided when the workflow was built. For a large share of work, that is exactly what you want.

When rules are enough

Rules are enough when the task is stable and the inputs are predictable. Move a row from one system to another, send a templated email when a deal closes, sync records on a schedule. The path never changes, so encoding it once is cheaper, faster, and easier to audit than asking a model to decide each time. If you can write the steps down completely, you probably do not need an agent.

The differences that matter

The split comes down to where the logic lives and what happens when the input changes.

• Control logic
• • Agentic workflow: Decided at runtime by the agent's reasoning
  • Deterministic workflow: Fixed at build time as explicit rules
• Handling change
• • Agentic workflow: Adapts to new or ambiguous inputs
  • Deterministic workflow: Breaks or needs a rule update when inputs change
• Tool use
• • Agentic workflow: Selects tools dynamically based on the goal
  • Deterministic workflow: Calls a predefined set of steps in order
• Memory and state
• • Agentic workflow: Carries state across steps and runs
  • Deterministic workflow: Stateless per run unless explicitly stored
• Governance and audit
• • Agentic workflow: Harder; behavior can vary per run
  • Deterministic workflow: Easier; the same path every time
• Best for
• • Agentic workflow: Ambiguous, variable, multi-step goals
  • Deterministic workflow: Stable, high-volume, auditable tasks

When to choose each

Choose an agentic workflow when the task is ambiguous, the inputs vary, and the right path is not known in advance: triaging a messy support queue, researching across sources, reconciling records that do not line up cleanly. Choose deterministic automation when the steps are stable, the volume is high, and you need the same auditable result every time. Most real systems are hybrids. An agent handles the judgment at the edges, and deterministic steps carry the predictable middle. The design question worth asking is which parts of a workflow deserve a model at runtime and which should be fixed in code. If you are ready to put one together, here is how to build an AI agent.

Agentic workflows trade predictability for flexibility, and the trade has real costs. Every run re-reasons, so token spend and latency climb with usage, and two runs of the same task can take different paths. That variance is what makes them harder to audit, which is why observability stops being optional once an agent can act on real systems. They also do not replace people: a human still owns the goal and the judgment calls above a threshold. None of this makes agentic workflows wrong. It means the autonomy should be earned and scoped, not assumed.

The Major take

Agentic workflows are genuinely useful and genuinely expensive to run at scale. Every execution re-reasons, which burns tokens, introduces variance between runs, and creates audit drift, the slow divergence between what you think the workflow does and what it actually did this time.

Major resolves this by separating exploration from exploitation. Let the agent reason through the ambiguous parts once, then compile the repeatable steps into a deterministic app with managed state, scoped credentials through the credential proxy, and audit at the point of action. The agent steps out, and the app runs without re-reasoning. State lives in managed databases and storage rather than a context window, and tool access is governable rather than ad hoc. The most valuable part of an agentic workflow is the reasoning you do once and never have to repeat. Reason once. Run forever.

If you have an agentic workflow that works in a demo but worries you at production scale, that is the case for compiling its repeatable steps into a governed app. Major's agents do the reasoning once and leave behind software your team can run and audit. Build your first agentic workflow on Major and keep the model on the judgment, not the busywork.

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