Workflows are predetermined sequences; agents make runtime decisions. The distinction matters because most production AI systems need both. Explore the inference.sh runtime →

A workflow is not an agent. An agent is not a workflow. Yet the two concepts get conflated constantly in conversations about AI systems. The confusion is understandable - both involve sequences of actions, both can call external services, both produce outputs from inputs. But conflating them leads to architectural mistakes, picking the wrong tool for the job, and building systems that are harder than they need to be. Understanding the distinction - and understanding when you need both - is essential for building AI systems that actually work.

The Core Distinction

The difference comes down to decision-making. A workflow is a predetermined sequence of steps. An agent is an entity that decides what steps to take.

When you define a workflow, you specify the exact sequence at design time. Step A runs, then step B, then step C. The order is fixed. The branching logic, if any, is explicit. You can look at a workflow and know exactly what will happen before it runs. The workflow does not decide anything - it executes what you designed.

When you deploy an agent, you specify capabilities and goals, not sequences. The agent has tools it can use, instructions it should follow, and objectives it should pursue. But the specific sequence of actions emerges at runtime based on the agent's reasoning about the current situation. You cannot look at an agent and know exactly what steps it will take - that depends on the input, the context, and the agent's judgment.

This distinction is not about complexity. Simple workflows exist. Complex workflows exist. Simple agents exist. Complex agents exist. The distinction is about where decisions happen - at design time or at runtime.

What Makes a Workflow

A workflow is a structured, repeatable sequence of steps whose outputs feed into subsequent steps. The key properties are predictability and repeatability. Given the same inputs, a workflow produces the same outputs through the same path every time.

Workflows excel when you have a process that should happen the same way consistently. Processing uploaded images through a standard pipeline - resize, compress, watermark - is a workflow. Onboarding new users through a defined sequence - create account, send welcome email, provision resources - is a workflow. Generating reports from data - extract, transform, aggregate, format - is a workflow.

The steps in a workflow might be complex. Each step might call sophisticated AI models, process large amounts of data, or interact with multiple external services. But the sequence of steps, the data flow between them, and the overall structure are determined when you build the workflow, not when it runs.

Modern workflow systems add capabilities that distinguish them from simple scripts. Durability means workflow state persists across failures - if step three fails, you can retry from step three rather than starting over. Observability means you can see exactly where a workflow is in its execution. Long-running execution means workflows can pause for hours or days waiting for external events, then resume. These properties make workflows suitable for serious production use, not just quick automation scripts.

What Makes an Agent

An agent is an entity that perceives its environment, makes decisions, and takes actions toward objectives. The key properties are autonomy and adaptability. Given the same inputs, an agent might take different actions depending on what it learns along the way.

Agents excel when you have tasks that require judgment and adaptation. Answering a customer question where the right answer depends on understanding the question, searching for relevant information, and synthesizing a response - that is agent work. Debugging a system where you need to investigate, form hypotheses, test them, and iterate - that is agent work. Conducting research where you need to find sources, evaluate their relevance, and adjust your search based on what you discover - that is agent work.

Agents have components that support this autonomous operation. Tools give agents the ability to take actions and gather information. Memory lets agents retain context across interactions. Reasoning lets agents evaluate options and make choices. Goals give agents objectives to pursue. The combination enables behavior that adapts to circumstances rather than following a predetermined path.

The agent might end up taking the same sequence of actions on similar inputs. But that sequence emerged from reasoning, not from predetermined structure. The agent could have done something different if the situation warranted it.

The Confusion

Several factors cause people to conflate workflows and agents.

Both involve sequences of steps. When you watch an agent work, you see a sequence of actions - it searches, then reads, then summarizes. This looks like a workflow executing. But the sequence was decided by the agent, not predetermined. The next run might produce a different sequence based on different inputs or intermediate results.

Marketing muddies the waters. Tools get called "agent platforms" when they are really workflow builders. Tools get called "workflow automation" when they involve agent-like decision making. The terminology is used loosely because both terms carry positive connotations.

The boundary can blur. A workflow with complex conditional logic starts to feel agent-like. An agent that always follows similar patterns starts to feel workflow-like. The concepts exist on a spectrum rather than being perfectly distinct categories.

Agentic steps in workflows exist. A workflow step might invoke an agent to handle a sub-task that requires judgment. The overall process is a workflow - predetermined steps in predetermined order - but individual steps involve autonomous decision-making. This hybrid pattern is genuinely useful but adds to the conceptual confusion.

When to Use Workflows

Reach for workflows when the process is known and should be consistent.

Repeatable procedures that should execute the same way every time belong in workflows. If you have documented the process, if deviation from the process is a problem, if consistency matters more than adaptation - use a workflow.

Compliance-sensitive processes where you need to prove what happened and in what order benefit from workflow structure. The predetermined nature of workflows makes them auditable and verifiable in ways that agent behavior is not.

Integration pipelines that move data between systems, transform formats, and synchronize state are workflow problems. The transformations are known. The sequence is fixed. Agent reasoning would add overhead without benefit.

Batch processing of many similar items through identical steps is workflow territory. Process a thousand images through the same pipeline. Send a thousand emails through the same template. The per-item decisions are minimal or nonexistent.

Workflows are also appropriate when predictability matters more than optimization. An agent might find a better approach to a task, but if you need to know exactly what will happen, a workflow provides that guarantee.

When to Use Agents

Reach for agents when the task requires judgment and the right approach depends on context.

Open-ended tasks without predetermined solutions need agents. Answering a question where you do not know what information will be needed. Solving a problem where the solution path is not known in advance. Creating something where the approach should adapt to the specific request.

Tasks requiring interpretation benefit from agent reasoning. Understanding what a user actually wants when their request is ambiguous. Evaluating whether search results are relevant. Deciding when enough research has been done.

Tasks with feedback loops where later actions depend on results of earlier actions suit agent architectures. The agent tries something, evaluates the result, and adjusts its approach. This adaptive behavior does not fit well into predetermined workflow structures.

Interactive tasks where the system responds to ongoing user input need agent-like flexibility. A conversation where each response depends on the current state. A collaborative session where direction emerges from interaction.

Agents are also appropriate when optimization matters more than predictability. An agent might find a better approach than a predetermined workflow would. If exploring that possibility is valuable, agents provide the flexibility to do so.

Why You Need Both

The choice between workflows and agents is not either-or for most real systems. Complex applications need both, working together.

Consider a content creation system. The overall process might be a workflow: research phase, then writing phase, then editing phase, then publication phase. That sequence is predetermined and should be consistent. But within the research phase, you need an agent that can search for information, evaluate what it finds, and adapt its search based on results. The editing phase might involve an agent that makes judgment calls about improvements. The workflow provides structure; the agents provide intelligence within that structure.

Or consider a customer service system. The triage step might be an agent that understands the request and decides which process applies. Then a workflow handles the specific process - refund workflow, technical support workflow, account change workflow. The workflow ensures consistency for each process type. The agent provides the judgment to route correctly.

This pattern - agents as steps within workflows, or workflows as tools available to agents - is where the real power lies. You get the predictability of workflows where you need it and the adaptability of agents where you need that. Neither alone covers all the cases a real system must handle.

A Unified System

The cleanest architectures treat workflows and agents as part of the same system rather than separate tools that happen to coexist.

In a unified system, an agent can call a workflow as a tool. When the agent needs to execute a defined process - process this image through the enhancement pipeline, run this data through the validation workflow - it invokes the workflow and receives the result. The agent makes the decision to use the workflow; the workflow executes the predetermined steps.

Conversely, a workflow can include agent steps. When a workflow reaches a step that requires judgment - analyze this document, classify this request, generate appropriate content - it invokes an agent. The workflow continues with the agent's output. The workflow maintains the overall structure; the agent provides the reasoning for that step.

This bidirectional integration means you choose the right tool for each part of your system without artificial constraints. Workflow when you need structure. Agent when you need judgment. Both working together in a coherent architecture.

The alternative - separate systems for workflows and agents that do not integrate cleanly - forces awkward handoffs, duplicated capabilities, and architectural compromises. You end up choosing one paradigm because you cannot use both effectively, even when both would serve you better.

Practical Implications

Understanding the distinction affects how you design systems.

Start by identifying which parts of your problem need judgment and which need consistency. Map out the overall process. Where are decisions made at design time versus runtime? This analysis suggests where workflows and agents each fit.

Design workflows for the structural backbone. The overall process flow, the guaranteed sequences, the compliance-critical paths - these belong in workflow structures where predictability is enforced.

Design agents for the judgment-requiring components. The interpretation, the adaptation, the decision-making - these belong in agent components where reasoning can happen.

Plan the interfaces between them. How do agents invoke workflows? How do workflows invoke agents? What data passes at each boundary? Clean interfaces make the hybrid architecture maintainable.

Accept that the boundary will evolve. As you learn more about your domain, some agent decisions might become predictable enough to move into workflows. Some workflow steps might reveal hidden complexity that benefits from agent judgment. The architecture should accommodate this evolution.

For teams building systems that need both structured processes and intelligent reasoning, inference.sh provides workflows and agents as part of the same unified platform. Flows define visual workflows with durable execution. Agents provide autonomous reasoning with tool use. Each can invoke the other. You build with both, integrated, choosing the right approach for each part of your system.

The question is not workflows or agents. The question is where in your system each one belongs. Getting that architecture right - structured where structure helps, adaptive where adaptation helps - produces systems that are both reliable and intelligent.

FAQ

Can a single task be both a workflow and an agent problem?

Yes, and this is common. The overall task might be a workflow - a defined sequence of phases - while individual phases involve agent reasoning. A content pipeline might have fixed phases (research, draft, review, publish) making it a workflow, but each phase involves judgment calls that benefit from agent capabilities. The workflow provides the skeleton; agents provide the intelligence at each step. Recognizing this hybrid nature leads to better architectures than forcing the entire task into one paradigm.

How do I decide if something should be a workflow step or an agent decision?

Ask whether the decision is the same every time or depends on context. If step B always follows step A for a given process, that is workflow structure. If what happens after step A depends on evaluating the results and choosing among options, that is agent territory. Another test: could you write explicit rules that handle all cases? If yes, a workflow with conditionals might suffice. If the rules would be incomplete or the exceptions numerous, agent judgment handles the complexity better.

What happens when workflows fail versus when agents fail?

Workflow failures are usually clear - a specific step failed with a specific error. The predetermined structure makes diagnosis straightforward. You know exactly where execution stopped and why. Agent failures can be subtler - the agent completed but made poor decisions, or got stuck in unhelpful loops, or missed better approaches. Debugging requires examining reasoning, not just execution logs. This difference affects monitoring strategies: workflows need step-level health checks, agents need reasoning quality assessment.