Guide
AI agents vs workflows vs agentic AI
Harrowtech.ai · last checked 13 October 2026
An AI agent is software in which an AI model decides its own next steps and uses tools to reach a goal. A workflow runs steps that were fixed in advance, even if some of those steps call an AI model. Agentic AI is the wider term for systems that plan and act with little human input, working through one or more AI agents. The rest of this page gives each definition in the words of the companies that build these systems, and a test to tell them apart.
The definitions, word for word
| Workflow | Anthropic: “systems where LLMs and tools are orchestrated through predefined code paths.” |
|---|---|
| AI agent | Anthropic: “systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.” Anthropic’s Agent SDK docs: “An agent is an application that completes a task by planning its own steps and calling tools that read files, run commands, or edit code.” Google Cloud: “AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt.” Its Cloud Run docs put it more briefly: “autonomous software entities that use LLM-powered systems to perceive, decide, and act to achieve goals.” Microsoft (Copilot Studio): “An agent is an AI assistant that handles conversations and completes tasks.” |
| Agentic AI | Google Cloud: “an advanced form of artificial intelligence focused on autonomous decision-making and action. Unlike traditional AI, which primarily responds to commands or analyzes data, agentic AI can set goals, plan, and execute tasks with minimal human intervention.” |
| Agentic systems | Anthropic’s umbrella term. It puts workflows and agents both under “agentic systems”, while drawing “an important architectural distinction” between them. |
The words do not line up perfectly across companies. Microsoft’s definition calls an agent an AI assistant, while Google Cloud treats assistants as a less autonomous kind of agent. Anthropic’s split between workflows and agents is the sharpest of the three, so it is the one used for the test below.
Agentic AI and AI agents: how they relate
Google Cloud puts it this way: “AI agents are the building blocks of agentic AI.” An agent “might focus on a specific task”, while “agentic AI employs multiple agents to handle complex workflows” and coordinates them. It also places agentic AI inside generative AI: generative AI creates content, and agentic AI “goes beyond content creation and function calling by executing actions in underlying systems to achieve higher-level goals.”
Google lists five parts to how agentic AI works: perception (gathering information), reasoning (an LLM working out what it means), planning, action, and reflection (checking whether the action worked and adjusting).
Agent, assistant or bot?
Google Cloud separates three terms that are often swapped:
- AI agent: purpose “Autonomously and proactively perform tasks”; can take “complex, multi-step actions” and make decisions independently.
- AI assistant: responds to requests and “can recommend actions but the user makes decisions”.
- Bot: “Follows pre-defined rules; limited learning; basic interactions”.
A three-question test
These questions follow from Anthropic’s definitions. Ask them of any system someone calls an agent.
- Who decides the next step: the code or the model? If the path is written down in advance, it is a workflow, however much AI sits inside the steps. If the model chooses which tool to use next, it is an agent.
- Can you say in advance how many steps it will take? Anthropic says agents suit “open-ended problems where it’s difficult or impossible to predict the required number of steps, and where you can’t hardcode a fixed path.”
- Where does a person step in? Anthropic says agents “can then pause for human feedback at checkpoints or when encountering blockers.” A workflow can have an approval step built into its fixed path.
Which one to use
- Start simple. Anthropic recommends “finding the simplest solution possible, and only increasing complexity when needed”, which “might mean not building agentic systems at all.”
- Workflows “offer predictability and consistency for well-defined tasks”. Repeat admin, such as raising an invoice, is usually a task of that kind.
- Agents cost more and can go wrong in a chain. Anthropic warns that “The autonomous nature of agents means higher costs, and the potential for compounding errors”, and recommends “extensive testing in sandboxed environments, along with the appropriate guardrails.”
- Agentic systems, in Anthropic’s words, “often trade latency and cost for better task performance”. Pay that only where the task needs it.
Google Cloud’s advice for agentic AI points the same way: clear objectives, good data, human oversight “to prevent unintended consequences”, and systems that can explain their decisions.
Worked example: a lead finder
Harrowtech.ai built a lead generation agent for Ovais Khan of saifcarpets.com. His review, from 4 June 2026, describes what it does:
In his words, it “runs every day, finds leads, qualifies them”, fills a spreadsheet and prepares a pre-written outreach email; he will “just review and set status to send”. His full review is on the AI automation agency page.
Read against the definitions, the review shows both kinds of trait:
- Fixed, workflow-style parts: it “runs every day”, its output always goes into a spreadsheet, and nothing is sent until he sets the status to send. Those are set in advance.
- Parts that vary with each lead: deciding whether a lead qualifies, gathering “as much info as possible”, and writing an outreach email. Writing a different email for each lead is the kind of task a language model suits.
- A human checkpoint: the send. The owner reviews each email and decides.
The client calls it an agent, which is the everyday use of the word. The review does not describe the internal steps, so it cannot settle whether the build is an agent or a workflow in Anthropic’s strict sense. For a business owner, the useful questions are the ones in the test above: which steps are fixed, where the AI uses judgement, and where a person approves.
That is how we build in general. Where a job can run on fixed rules, it does, with no AI, so it gives the same answer every time; AI is used only where it helps; and your approval sits in front of anything that leaves. See how we work.
Further reading
- AI agent architecture: the parts and how they fit: the model, tools, memory, loop and guardrails inside an agent, and what to decide before building one.
- Anthropic AI agents: the products Anthropic offers for building agents, and its five workflow patterns.
- Microsoft Copilot agents: building agents in Copilot Studio and Agent Builder.
- n8n, Zapier and Make compared: the tools most small-business workflows and agents are built in.
- Is AI automation safe?: prompt injection and the other risks that grow as an agent gets more freedom.
Sources
- Anthropic, Building effective agents (19 December 2024; the post now notes that much of the tooling it describes has changed)
- Anthropic, Agent SDK overview
- Google Cloud, What are AI agents? (last updated 2 April 2026)
- Google Cloud, What is agentic AI? (last updated 16 September 2026)
- Google Cloud, Host AI agents on Cloud Run (last updated 9 October 2026)
- Microsoft Learn, Copilot Studio overview (25 September 2026)
- Client review: Harrowtech.ai home page
All read 13 October 2026.