An agentic workflow is a sequence of tasks that an AI carries out on its own, without someone manually stepping through each stage.

Instead of just answering a question, the AI decides what to do next, uses the tools available to it, acts on the result, and keeps going until the job is done. In a business context that means the AI can read an incoming email, look up the relevant customer record, draft a reply, and log the interaction - all without anyone clicking a button.

The key distinction from a standard automation is that agentic workflows can handle variation and make decisions mid-process, rather than only following a fixed script.

A non-agentic workflow follows a fixed, pre-defined sequence. Every step is mapped out in advance, and if something unexpected happens, the workflow either fails or passes the problem back to a human. An agentic workflow is different because the AI can figure out the steps as it goes. It reads the current situation, decides what action makes sense, carries it out, checks the outcome, and adjusts if needed. Non-agentic automation is reliable for processes that never change.

Agentic workflows are useful when the inputs vary, when judgment is involved, or when you want the AI to handle exceptions without always escalating to a person.

Agentic AI generally operates in four stages. First, perception: the agent takes in information from its environment, whether that is an email, a database record, a calendar entry, or a user message. Second, reasoning: it processes that information and works out what needs to happen, drawing on its instructions and any context it has been given. Third, action: it carries out the required steps using whatever tools it has access to, such as sending a reply, updating a record, or running a search. Fourth, reflection: it checks whether the action produced the right result and, if not, tries again or flags the issue.

These four stages repeat in a loop, which is what allows an agentic system to handle multi-step tasks from start to finish.

Standard ChatGPT is not agentic in the full sense. By default it responds to a single message and then stops. It does not take ongoing action, monitor anything, or connect to your data unless you have specifically set up integrations. There are agentic modes and plugins that extend it, and OpenAI has built separate tools aimed at agentic use, but the chat interface most people use is a conversational assistant, not an agent.

An agentic system built for your business is connected to your actual systems, runs in the background, and continues working without you prompting it each time.

AI agents are generally categorised by how they make decisions.

Simple reflex agents react to the current input using fixed rules.

Model-based reflex agents keep track of context so they can handle situations where the current input alone is not enough.

Goal-based agents work backwards from a target outcome to choose the right action.

Utility-based agents weigh up different options and pick the one most likely to produce the best result. Learning agents improve over time based on feedback.

Hierarchical agents break complex tasks into sub-tasks, sometimes using other agents to handle each part.

Multi-agent systems involve several specialised agents working together, each handling a different part of a larger process.

In practice, most business AI workflows combine elements from several of these categories.

The four pillars that make an AI agent functional are: perception (the ability to take in information from external sources), memory (storing context so the agent can refer back to earlier steps or past interactions), action (the ability to actually do things, not just produce text), and reasoning (working out the right course of action given the current situation and goal).

Without all four, the agent cannot complete real tasks autonomously. A system that can only reason but cannot act is just a question-answering tool. One that can act but cannot reason will follow instructions rigidly and fail when anything falls outside its script.

Some platforms offer free tiers that let you experiment with basic AI agent setups.

Tools like n8n (self-hosted), Make, and several others have free plans with usage limits. The underlying AI models that power the agents typically carry a cost once you move beyond the free usage allowances, and hosting adds to that. For a personal project or early prototype, free tools can take you a reasonable distance.

For a business workflow that runs continuously and connects to live data, you will generally need to budget for both the platform and the model usage. The costs are more accessible than they used to be, but a production-grade agentic system built around your real business data is not typically free to run.

Technically yes, ChatGPT's underlying model can be used as the reasoning layer inside an agent, but ChatGPT on its own is not a complete agent-building platform.

To build something that actually works for your business, you need to connect it to your tools (CRM, email, calendar), give it secure access to take actions in those systems, and write the orchestration logic that keeps it running correctly. That involves API integrations, prompt engineering, error handling, and ongoing maintenance.

For business owners, this is where it gets fussy fast. What starts as an interesting experiment turns into a time-consuming technical project, and a poorly built agent that connects to live business data is a security risk if it is not set up properly.

Having a specialist like Ash Glover build and manage it for you means you get a stable, secure system without having to become a developer to get there.

An AI agent developer builds systems that allow AI to take action on behalf of a business, rather than simply answering questions.

The work covers designing the workflow logic, connecting the AI to your actual tools and data sources (your CRM, email, calendar, accounts), writing the instructions that govern how the agent behaves, and testing it thoroughly against real inputs before it runs unsupervised. It is practical, integration-focused work that sits at the intersection of software development and a detailed understanding of how language models behave in production.

For a business owner, the value of working with an AI agent developer like Ash Glover is that you get a system that is built properly from the start: secure, stable, and tailored to how your business actually operates, without spending weeks trying to figure it out yourself.

Building an AI workflow starts with mapping the process you want to automate: each step, what triggers it, what data it needs, and what a good result looks like.

Then you identify which steps involve judgment or variation, since those are where AI does more than a basic rule-based automation could. The technical side involves connecting an AI model to the tools it needs to act on (email, CRM, calendar, databases), writing the instructions that govern its behaviour, and building the logic that keeps it running reliably.

In practice, getting this right takes longer than most business owners expect. The connections need to be secure, the agent needs to handle edge cases without failing silently, and the instructions need to be precise enough that the AI does not go off-script.

For most businesses, the faster and lower-risk route is to work with a specialist. Ash Glover builds custom AI workflows connected to your existing systems, so you get something that works from day one without the back-and-forth of building it yourself.

An AI workflow engineer designs and builds the systems that connect AI models to real business processes. The role involves understanding what a business needs to automate, mapping those requirements into a technical design, building the integrations between the AI and the tools it needs to work with, and making sure the whole system behaves reliably.

It is less about training models from scratch and more about getting existing models to do useful, accurate work within a specific operational context. The skill set typically combines software development, knowledge of workflow and automation platforms, and experience working with AI APIs.

A workflow developer builds the automated processes that connect different systems and tools in a business. In an AI context, that means designing the logic that decides when the AI runs, what information it receives, what actions it can take, and how the outputs feed back into the business.

Day to day, this involves configuring integrations, writing and refining the instructions that guide the AI, testing edge cases, and maintaining the system as the business's needs change.

It is applied, practical work focused on making operations run more smoothly rather than building new AI technology from the ground up.