What is an agentic workflow?

An agentic workflow is a system in which a language model and its tools are orchestrated through predefined code paths to complete a task. A developer designs the structure, the sequence of steps, the routing, the points where the model reads or writes, and the model performs the steps inside it that require judgment. The distinction that matters, drawn from Anthropic's engineering guidance, is between a workflow and an autonomous agent: a workflow follows a path the developer set in advance, while an agent directs its own process and tool use at runtime. Most production AI systems are workflows. They are more predictable, easier to test, and simpler to govern than a fully autonomous agent, and they are the right design for any task whose steps can be drawn in advance.

What is the difference between an agentic workflow and an AI agent?

The difference is who controls the path. In an agentic workflow, a developer defines the code path in advance and the model executes steps within that fixed structure. In an autonomous agent, the model dynamically directs its own process and decides which tools to use at runtime, keeping control over how it accomplishes the task. A workflow is predictable and auditable because its route is known; an agent is flexible because its route is not, at the cost of predictability. Anthropic's Building Effective Agents draws exactly this line. The practical takeaway is to use a workflow whenever the steps can be specified ahead of time, which covers most operational AI work, and reserve an autonomous agent for open-ended tasks where the path genuinely cannot be predicted.

What are the common agentic workflow patterns?

Anthropic's engineering guidance describes five common workflow patterns. Prompt chaining decomposes a task into sequential steps where each model call processes the previous output. Routing classifies an input and directs it to a specialized follow-up path built for that category. Parallelization runs independent subtasks at the same time, or runs the same task several times for diverse outputs that are then aggregated. Orchestrator-workers uses a central model to break a task into subtasks at runtime, delegate them to worker models, and synthesize the results. Evaluator-optimizer pairs a model that generates a response with a second model that evaluates it and returns feedback in an iterative loop. These are composable building blocks, not a menu to pick one from, and real systems often combine several.

How do agentic workflows differ from traditional workflow automation like RPA?

Traditional workflow automation, including robotic process automation and business process management, executes rules a developer wrote in advance. It is fast and exact when every input matches the expected shape and it fails when an input falls outside that script, because both the path and the logic are fixed. An agentic workflow still runs a designed path, but a language model performs the steps that need judgment, such as reading unstructured text, classifying an ambiguous case, or drafting a response. The structure is fixed while the reasoning inside each step is not. In practice the two work together: keep deterministic automation for the predictable, high-volume parts of a process and add model-driven steps only where variability and judgment live.

When should I use an agentic workflow instead of an autonomous agent?

Use an agentic workflow whenever the path can be known in advance. If you can draw the steps, encode them as a workflow: it is easier to test, lighter to run, more predictable, and far simpler to audit than an agent choosing its own route. Reserve a fully autonomous agent for tasks where the number and order of steps are genuinely open-ended and the route cannot be predicted, because that flexibility trades away predictability and adds cost. Anthropic's guidance is to find the simplest solution that works and add complexity only when a simpler design falls short. Often that simplest solution is a single well-constructed model call, then a workflow, and only then an agent. Most operational AI work has a knowable path and belongs in a workflow.

Are agentic workflows the same as multi-agent systems?

No. A multi-agent system is a specific design in which several agents, often specialized, coordinate to complete work, and an orchestrator-workers workflow is one structured way to arrange that coordination. Agentic workflows are the broader control-flow taxonomy: the patterns, such as prompt chaining, routing, and parallelization, that govern how a model and its tools move through a task, whether one model or several are involved. A multi-agent system may be built out of workflow patterns, and a workflow may involve a single model with no additional agents at all. The two concepts sit at different levels: workflows describe control flow, multi-agent systems describe how multiple autonomous agents divide and coordinate work.

How do I adopt agentic workflows safely?

Adopt agentic workflows the way you would any system that acts on real data. Start with the simplest design that works and add structure only when a simpler one falls short. Instrument every step so you can see what the model did and why, set explicit stopping conditions that halt a run and escalate when something falls outside its mandate, and keep a person accountable for the outcome rather than watching every action. A workflow that reads and writes to real business systems needs the same governance discipline as an autonomous agent, sized to what it is permitted to do. Building that discipline into the design from the start, rather than adding it after a pilot succeeds, is how a workflow becomes something an organization can trust in production.

RL Written by Robert LabardeeFounder and CEO

Last updated July 2026.

What are agentic workflows?

Lightbridge Labs defines an agentic workflow as a system where a language model and its tools run through predefined code paths to complete a task. Unlike a fully autonomous agent that directs its own process at runtime, a workflow follows a structure a developer designed. Most production AI work is a workflow, not an agent.

Put a workflow to work Start with the paradigm

An agentic workflow follows a designed path; an agent directs its own.

The most useful distinction in building AI systems separates two things that get lumped together. A workflow is a system where a language model and its tools are orchestrated through predefined code paths that a developer designed. An autonomous agent is a system where the model dynamically directs its own process and tool use at runtime, keeping control over how it accomplishes the task. Anthropic draws this line in its engineering guidance, and it is the frame this page uses throughout.

The distinction is not academic. A workflow is predictable, easy to test, and simple to audit because its route is known in advance. An agent is flexible because its route is not, and it pays for that flexibility with less predictability and higher cost. Most production AI systems are workflows, not agents. This page owns the workflow side: the control-flow patterns that put a model to work. For the wider idea these patterns belong to, read the Lightbridge Labs guide on what agentic AI is. For the mechanics of a single autonomous agent, see how to build an AI agent.

Agentic workflows are built from a small set of control-flow patterns.

Anthropic's Building Effective Agents describes five workflow patterns that recur across production systems. They are composable building blocks, not a menu to choose one item from, and real systems often combine several. Understanding them is how a team designs a workflow deliberately instead of reaching for a fully autonomous agent by default.

Prompt chaining runs steps in sequence

The task is split into fixed steps where each model call processes the previous output. Draft, then critique, then rewrite. Prompt chaining suits work that decomposes cleanly into an ordered sequence, and it trades a little latency for higher accuracy on each isolated step.

Routing sends inputs to the right path

A model classifies an input, then directs it to a specialized follow-up path built for that category. A support request routes to billing, technical, or account handling. Routing separates concerns so each path stays focused instead of one prompt trying to cover every case at once.

Parallelization runs subtasks at the same time

Independent subtasks run simultaneously and their results combine, or the same task runs several times for diverse outputs that get aggregated. Parallelization cuts wall-clock time on work that splits cleanly and improves reliability when several passes vote on an answer.

Orchestrator-workers delegates dynamically

A central model breaks a task into subtasks at runtime, hands each to a worker model, and synthesizes the results. Unlike parallelization, the subtasks are not fixed in advance. This pattern fits work where the shape of the task is not known until the input arrives.

Evaluator-optimizer refines through feedback

One model generates a response while a second evaluates it and returns feedback, looping until the output meets a bar. Evaluator-optimizer suits work with clear evaluation criteria where an iterative pass measurably improves the result, such as translation or code that must satisfy a test.

The orchestrator-workers pattern is where a workflow starts to resemble a system of coordinating agents. When several specialized agents divide and coordinate work, that is a distinct design covered in the Lightbridge Labs guide on multi-agent systems. The libraries and platforms that implement these patterns are mapped in the AI agent tools and platforms landscape.

Agentic workflows differ from RPA in where the judgment lives.

Organizations already run workflow automation. Robotic process automation and business process management have moved rule-based work for years. The clearest way to place an agentic workflow is against them: same idea of a designed path, different engine inside the steps that need judgment.

Traditional automation follows fixed rules

Robotic process automation and business process management execute rules a developer wrote in advance. They are fast and exact when every input matches the expected shape, and they stall the moment an input falls outside the script. The path and the logic are both fixed.

Agentic workflows drive steps with a model

An agentic workflow still runs a designed path, but a language model performs steps that need judgment: reading unstructured text, classifying an ambiguous case, drafting a response. The structure is fixed; the reasoning inside each step is not. That is what lets it handle inputs rules cannot.

The two belong together

Strong operations keep deterministic automation for the predictable, high-volume parts and add model-driven steps only where variability and judgment live. Drawing that boundary deliberately is the design decision that decides whether an AI workflow automation program pays off or adds fragile complexity.

This is what people mean by AI workflow automation: a designed process with model-driven steps where rules alone cannot cope. When those workflows touch an operational or financial system such as an ERP, the automation belongs to the platform that owns it, and Lightbridge ERP covers AI inside NetSuite; workflows that touch a CRM such as Salesforce belong to Lightbridge Cloud.

Choose the simplest design that works, which is usually a workflow.

Anthropic's guidance is to find the simplest solution possible and add complexity only when a simpler one falls short. That single principle answers most design questions about agentic workflows. Lightbridge Labs applies it as an ordered set of choices, from a single model call up to a fully autonomous agent, adding structure only when the work demands it.

Start with the simplest thing that works

A single model call with good context and retrieval solves more problems than teams expect. Reach for a workflow only when one call falls short, and reach for a fully autonomous agent only when the path genuinely cannot be known in advance. Complexity is a cost you add when a simpler design fails.

Choose a workflow when the path is knowable

If you can draw the steps in advance, encode them as a workflow. A predefined path is easier to test, lighter to run, more predictable, and far simpler to audit than an agent deciding its own route. Most operational AI work has a knowable path and belongs here.

Choose an agent when the path is not

When the number of steps is open-ended and the route cannot be predicted, a workflow becomes a constraint and an autonomous agent earns its added cost. That flexibility trades away predictability, so it is a deliberate choice for the tasks that truly need it, not a default.

Adopt it under measurement and guardrails

Whichever design you pick, instrument every step, set a clear stopping condition, and keep a person accountable for the outcome. A workflow that acts on real systems needs the same governance discipline as an agent, sized to what it is allowed to do.

The reflex to build a fully autonomous agent for every problem is the most common way an AI project acquires cost and fragility it did not need. A workflow that runs a knowable path is leaner, more predictable, and easier to trust, and it is the right answer far more often than the agent hype suggests. Lightbridge Labs designs to the task, not to the trend, through its AI agents practice.

Vendor note: agent tooling and model capabilities move quickly. Verify current model and product details at docs.anthropic.com before planning around a specific feature. Where a model is named, the current Claude lineup is Fable 5, Opus 4.8, Sonnet 4.6, and Haiku 4.5, ordered by capability tier rather than price.

Agentic workflows: frequently asked questions

What is an agentic workflow?: An agentic workflow is a system in which a language model and its tools are orchestrated through predefined code paths to complete a task. A developer designs the structure, the sequence of steps, the routing, the points where the model reads or writes, and the model performs the steps inside it that require judgment. The distinction that matters, drawn from Anthropic's engineering guidance, is between a workflow and an autonomous agent: a workflow follows a path the developer set in advance, while an agent directs its own process and tool use at runtime. Most production AI systems are workflows. They are more predictable, easier to test, and simpler to govern than a fully autonomous agent, and they are the right design for any task whose steps can be drawn in advance.
What is the difference between an agentic workflow and an AI agent?: The difference is who controls the path. In an agentic workflow, a developer defines the code path in advance and the model executes steps within that fixed structure. In an autonomous agent, the model dynamically directs its own process and decides which tools to use at runtime, keeping control over how it accomplishes the task. A workflow is predictable and auditable because its route is known; an agent is flexible because its route is not, at the cost of predictability. Anthropic's Building Effective Agents draws exactly this line. The practical takeaway is to use a workflow whenever the steps can be specified ahead of time, which covers most operational AI work, and reserve an autonomous agent for open-ended tasks where the path genuinely cannot be predicted.
What are the common agentic workflow patterns?: Anthropic's engineering guidance describes five common workflow patterns. Prompt chaining decomposes a task into sequential steps where each model call processes the previous output. Routing classifies an input and directs it to a specialized follow-up path built for that category. Parallelization runs independent subtasks at the same time, or runs the same task several times for diverse outputs that are then aggregated. Orchestrator-workers uses a central model to break a task into subtasks at runtime, delegate them to worker models, and synthesize the results. Evaluator-optimizer pairs a model that generates a response with a second model that evaluates it and returns feedback in an iterative loop. These are composable building blocks, not a menu to pick one from, and real systems often combine several.
How do agentic workflows differ from traditional workflow automation like RPA?: Traditional workflow automation, including robotic process automation and business process management, executes rules a developer wrote in advance. It is fast and exact when every input matches the expected shape and it fails when an input falls outside that script, because both the path and the logic are fixed. An agentic workflow still runs a designed path, but a language model performs the steps that need judgment, such as reading unstructured text, classifying an ambiguous case, or drafting a response. The structure is fixed while the reasoning inside each step is not. In practice the two work together: keep deterministic automation for the predictable, high-volume parts of a process and add model-driven steps only where variability and judgment live.
When should I use an agentic workflow instead of an autonomous agent?: Use an agentic workflow whenever the path can be known in advance. If you can draw the steps, encode them as a workflow: it is easier to test, lighter to run, more predictable, and far simpler to audit than an agent choosing its own route. Reserve a fully autonomous agent for tasks where the number and order of steps are genuinely open-ended and the route cannot be predicted, because that flexibility trades away predictability and adds cost. Anthropic's guidance is to find the simplest solution that works and add complexity only when a simpler design falls short. Often that simplest solution is a single well-constructed model call, then a workflow, and only then an agent. Most operational AI work has a knowable path and belongs in a workflow.
Are agentic workflows the same as multi-agent systems?: No. A multi-agent system is a specific design in which several agents, often specialized, coordinate to complete work, and an orchestrator-workers workflow is one structured way to arrange that coordination. Agentic workflows are the broader control-flow taxonomy: the patterns, such as prompt chaining, routing, and parallelization, that govern how a model and its tools move through a task, whether one model or several are involved. A multi-agent system may be built out of workflow patterns, and a workflow may involve a single model with no additional agents at all. The two concepts sit at different levels: workflows describe control flow, multi-agent systems describe how multiple autonomous agents divide and coordinate work.
How do I adopt agentic workflows safely?: Adopt agentic workflows the way you would any system that acts on real data. Start with the simplest design that works and add structure only when a simpler one falls short. Instrument every step so you can see what the model did and why, set explicit stopping conditions that halt a run and escalate when something falls outside its mandate, and keep a person accountable for the outcome rather than watching every action. A workflow that reads and writes to real business systems needs the same governance discipline as an autonomous agent, sized to what it is permitted to do. Building that discipline into the design from the start, rather than adding it after a pilot succeeds, is how a workflow becomes something an organization can trust in production.

This guide is independent, general educational information published by Lightbridge Labs. Claude and Anthropic are trademarks of Anthropic, PBC. Lightbridge Labs is not affiliated with, endorsed by, or a partner of Anthropic, PBC.

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