AI workflows vs. agents: Similarities, differences, and when to use each
Overview
Both AI agents and AI workflows can automate tasks using generative AI, but they work in fundamentally different ways. This guide breaks down how each approach works, where they excel, and how to decide which fits your use case.
On the surface, AI agents and AI workflows look similar. They’re both types of automation using generative AI, but there are distinct differences in how they work and what outcomes they’re capable of achieving. Workflows consist of a predefined set of steps with predictable outcomes, while AI agents work autonomously, within a set of rules, to achieve a goal.
AI agents are more flexible than workflows, but also more complex to build and resource intensive. Think of workflows as following a recipe exactly—you get consistent, predictable results every time, and anyone can execute the steps successfully. AI agents are more like a chef who’s able to adjust based on what ingredients are available and what’s required.Knowing the differences between the two approaches will help you decide when to use one, the other, or both together.
Are AI agents just workflows?
AI agents and AI workflows both aim to automate processes, but take different approaches. AI workflows incorporate generative AI as a step in a linear, predefined process. In a customer support workflow, you might pass tickets to an LLM for initial review and triaging before sending them on to a human support agent to respond. The AI logic accelerates the process and enhances human decision making, saving time and effort.
AI agents go a step further, by automating the entire process from end to end. In our customer support workflow, the AI agent performs both triage and response. The agent resolves common issues by generating personalized responses or taking direct action (such as resetting a password or retrieving order details). If the task isn’t straightforward, the agent can ask follow-up questions, or escalate unclear or high-risk requests to a human support agent.
The process isn’t predefined—the agent defines it in response to the task or prompt. Humans can still have oversight and step in when the agent fails, but there’s no human-driven step required to execute or complete the process.
Why this distinction matters
The distinction between AI agents and AI workflows is often blurred. It’s common for people to assume that the presence of an LLM in a workflow indicates that it’s an AI agent. But an LLM alone doesn’t define the system, the architecture does. AI agents have autonomy over their actions and the tools they use to carry them out.
AI agents are powerful, but more resource intensive and less predictable than workflows. Choosing the right AI tool will depend on what you’re trying to achieve. If you have a process that’s already well defined, an AI workflow may be sufficient. You also might prefer workflows if you want to protect against misguided advice when safety or accuracy is particularly important.
For more ambiguous and open-ended tasks, an AI agent can fully take over, freeing up humans to focus on other business goals.
What is an AI workflow?
An AI workflow is a predefined sequence of steps where generative AI handles specific tasks—like summarizing text, classifying data, or drafting responses—within a larger automated process. The workflow itself is scripted by a human; the AI just makes certain steps smarter.
Structured automation and orchestration
In an AI workflow, generative AI is integrated into one or more steps of a defined process. With triggered pipelines, conditionals, and retries all clearly defined at the outset, the overall process is deterministic: meaning that the same input will always result in the same output.
These workflows are often designed to be stateless. Each execution is self-contained, and does not retain memory of previous runs beyond what is explicitly passed as input or stored externally. This architecture allows for easier debugging, scaling, and parallelization.
AI-enhanced workflows are great for things like support ticket routing, data processing jobs like ETL, and multi-step business logic such as onboarding flows.
Benefits of AI workflows
AI workflows are more straightforward to implement compared to building and deploying AI agents, and "can provide a quick win to automate repeatable and clearly-defined processes." AI workflows’ deterministic nature makes them easier to debug, since the process is composed of transparent, independent steps that are easily traced by observability tools you’re probably already using.
AI enhancements are small tweaks applied selectively to repetitive or rules-based tasks, so less preparation, preparation, and training is needed to make them production ready.
What is an AI agent?
AI agents represent a shift from automation that follows instructions to automation that figures out the instructions itself. Rather than scripting every step, you give an agent a goal and let it determine how to get there.
Autonomous decision-making loop
An AI agent is a software system that autonomously decides what actions to take to achieve a goal, and carries out those steps by interacting with a predefined set of tools. In AI workflows, AI is used as one (or multiple) steps in a series of explicit instructions, but with agents, AI both defines the process and carries it out.
Because AI agents are designed to mimic human thinking and problem solving, they require a variety of systems to function:
- Memory to retain context across multiple steps in the process
- A planning module so that it can reason about the best way to address a problem
- Integrations with relevant data sources and tools
An agent uses the LLM as a reasoning engine, so there’s less demand on humans to decide how to approach a task. Ambiguous tasks that were previously impossible to automate can be handed over to AI agents. While humans can (and should) oversee AI agents’ actions, agents can take on much of the heavy lifting in tasks like research, triaging, and customer support.
Benefits of AI agents
Agents excel at open-ended assignments where there are many variables and possible ways to approach a problem. Automating tasks with AI workflows hits a roadblock when handling ambiguity or unstructured input, but agents have the flexibility to handle it. Capable of applying complex decision-making over long (and changing) contexts, agents can self-correct or replan mid-task in response to new information or user feedback.
AI agent vs AI workflow: Core differences
Understanding when to use an agent versus a workflow comes down to a few key trade-offs. Here's how they differ across the dimensions that matter most for building reliable, cost-effective automations.
Control flow
Think of it this way: you tell a workflow what to do, whereas you tell an agent what you want to accomplish, and then the agent decides what to do to achieve that goal.
Workflows are structured and explicitly defined up front, including any branches or triggers—even where generative AI is included as a step in the process. There is a predictable, repeatable series of actions that happen in sequential order, and the same inputs will result in the same outputs.
Agents are nondeterministic and flexible. They decide what steps to carry out and in what order, they react to changing conditions, and they can iterate or deviate dynamically. Agents’ outcomes aren’t predictable, but they are far more flexible and capable of completing open-ended tasks where the path to success isn't clear upfront.
Observability and debugging
Workflows are easy to audit, debug, and maintain, because the process is transparent and composed of sequential steps. Step outputs enable easy logging using existing tooling, and reproducing bugs is simple when retries generate the same output each time. This is ideal for scenarios demanding a high level of reliability and compliance—like automatically processing invoices through an approval chain or running scheduled data transformations.
Agents may take many different paths to reach a goal, and their reasoning or intermediate steps aren’t necessarily captured. This can make agents harder to debug, unless you intentionally set up agent architecture that treats every run as a replayable event, where you can watch the agent’s reasoning unfold step-by-step, inspect why it selected specific tools, and rewind to any decision point.
Why are AI workflows easier to debug than AI agents?
AI workflows are scripted. Meaning, what happens and under what conditions is consistent and predictable. Agents make their own decisions about what to do next, and this decision-making process isn't visible by default—so unless you set up detailed logging to track their reasoning, decision making happens in a black box.
State and memory
Typically, AI workflows are designed to be stateless and self-contained, unless state is persisted specifically. Agents need internal short-term memory (like scratchpads) to retain context across steps and replan or improve if necessary. This working memory makes agents more flexible and adaptable than workflows, but is often harder to monitor.
Latency and cost
With a predefined sequence of steps, workflows’ execution time and resource usage is predictable, making it easier both to estimate costs and infrastructure needs. AI agents make decisions dynamically, so it’s impossible to know exactly what number of steps they will take or how many LLM calls might be required. Unpredictable run times and resource usage lead to variable costs.
When to use an AI agent or AI workflow?
Which model you choose will depend on your use case and what you’re optimizing for.
Use cases that favor workflows
Workflows suit tasks and processes with well-defined inputs and outputs, and are especially useful in scenarios with a low tolerance for unpredictability. If a workflow designer can easily express the entire process as a series of sequential steps, with little variation and branching required, an AI workflow should be sufficient. Regulated industries with compliance or audit requirements will find workflows easier to monitor and manage than agents.
A sales team might use a workflow to automatically enrich new leads with data like their company size, vertical, or job title; summarize key details; and assign them to the right rep based on fixed rules.
When should you use an AI agent instead of a workflow?
You could ask an agent to research a new lead across multiple sources, decide whether they fit your ICP, and draft a tailored outreach email—choosing which steps to take and how deeply to dig based on what it finds.
AI agents are best suited for ambiguous, high-context, or open-ended tasks where there’s no standard playbook, and multiple possible paths to resolution. Agents like user-facing copilots, decision support agents, or research assistants allow you to automate processes within systems that need real-time adaptation, like responding dynamically to new inputs and collating up-to-date information from multiple sources.
Hybrid patterns
A combination of AI agents and workflows can give you the best of both, for cases that need both the predictability of workflows and the flexibility of agents. You can assign planning tasks to an agent, while a workflow handles execution. In a customer support triage flow, a workflow can take care of standard steps (like categorizing tickets or resetting passwords), but trigger the agent module for reasoning when encountering ambiguity (like missing labels or unclear language on a ticket).
Design considerations for engineers
Whether you're building a workflow, an agent, or a hybrid of both, a few architectural decisions will determine how reliably your system runs in production. Here's what to plan for.
Error boundaries and fail-safes
Both AI workflows and AI agents need fallback options in case something goes wrong. For workflows, that means building in retry logic to automatically execute failed steps again, and making sure that repeated failures generate meaningful error messages for debugging.
Guardrails and fail-safes for agents are especially important because agents don’t follow a pre-determined set of steps to solve problems. Constraint layers prevent undesirable actions or unintended consequences of agents interacting with parts of your system that they shouldn’t. That might mean limiting an agent to interacting only with approved APIs or databases, or requiring additional validation or explicit human approval for high-risk actions like deleting records.
Tooling choices
Your AI stack will dictate what you can achieve—both in terms of utility and performance. Retool offers two helpful primitives—Agents for autonomous, stateful problem-solving and Workflows for deterministic automations—often used together as needs evolve.
Retool Workflows offer a fast way for developers to build automated processes (like cron jobs, custom alerts, and ETL tasks), integrating generative AI blocks where needed without starting from scratch.
Retool Agents let developers build long-running, AI-powered backends that can call APIs, query databases, and make decisions dynamically. They can maintain state across steps, adapt their approach based on real-time conditions and data, and are governed by Retool's role-based permissions to ensure safe, controlled access to data and systems.
Infrastructure will also influence your AI stack—both your existing setup and what you’re prepared to invest in. Flexible, agent-centric systems often need more scalable, on-demand compute power than traditional IT infrastructure can support, while workflows with an AI component are less resource intensive and may work with modifications to existing infrastructure (like scaling up data storage).
Where we’re headed
The field is rapidly converging toward hybrid architectures where the decision is not simply 'workflows or agents' but rather how to best apply AI to solve business problems.
Rather than starting with a particular AI approach and fitting it to your use case, the most successful implementations begin with clearly defined business outcomes and then choose the AI architecture—whether workflows, agents, or hybrid approaches—that best delivers those results.
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