Short answer: an agentic AI workflow is software that takes a sequence of actions across your tools, such as reading a request, checking a system, drafting a response and routing it, with a person approving the steps that matter. In a mid-market business it works on narrow, high-volume processes. It isn’t yet an autonomous digital employee.
“Agentic” is on every vendor slide this year. Boards are asking about it, and some leadership teams are being pitched AI agents that will run whole functions. Most of that is ahead of where the technology, and most companies’ data, actually is.
Gartner is blunt about it. In June 2025 it predicted that over 40% of agentic AI projects will be cancelled by the end of 2027, because of escalating costs, unclear business value or inadequate risk controls. Gartner also warned about “agent washing”, where existing chatbots, assistants and RPA tools are rebranded as agents, and estimated that only about 130 of the thousands of vendors claiming agentic AI are the real thing.
That’s not a reason to wait. It’s a reason to be specific. I’ve designed and built these workflows, including as founder and CEO of a systems integrator built around agentic AI and automation. Here’s what I think is real.
What an agentic workflow actually is
Strip away the marketing and an agentic workflow is software that takes a sequence of actions across several tools to finish a piece of work, with a person in the loop.
The difference from traditional automation is what happens between the steps. A rules-based workflow does the same thing every time. An agentic one can read something messy, like an email, a contract or a scanned invoice, work out what it is, choose the next step and call the right system. A person approves anything costly, external or hard to undo.
A simple example: a supplier emails an invoice. The workflow reads it, matches it to the purchase order in your finance system, flags any difference, drafts a query to the supplier if something’s wrong, and puts the matched invoice in front of someone to approve. Nobody re-keys anything. Nobody chases. A person still signs off the payment.
That’s useful. It’s also a long way from “an AI agent that runs accounts payable”.
Automation first, then agents
It’s worth being precise here, because the line gets blurred.
At a global consumer brand, I turned the product launch process into a single automated workflow. One intake form now triggers 450 launch tasks across 8 teams, 4 regions and 14 agencies. That’s workflow automation rather than AI: the sequence is fixed, and the platform routes each task to the right team, region or agency.
It still matters for agentic AI, because it’s the foundation. I had to document the launch sequence before I could automate it. AI agents need the same thing: a known process, clear owners and systems they can connect to. Much of the value in a first agentic workflow comes from that groundwork, with AI taking on the steps that used to need a person to read and decide.
The AI example is translation at scale. At the same brand, I moved translation and localisation onto an AI-first platform across 14 languages, with people reviewing and refining rather than translating from scratch. It saves around US$1M a year. The human review step is part of the design, not an afterthought. (More on my work with consumer brands and retailers.)
Where agentic workflows work today
They work best when four things are true: the work is high-volume and repetitive, the inputs are messy enough that simple rules struggle, the systems involved can be connected, and a mistake can be caught before it costs much.
| Good first agentic workflows | Why they work | Watch out for |
|---|---|---|
| Invoice and document intake | High volume, a clear right answer and an existing system to check against | Changes to supplier bank details should always need a person to verify them |
| Customer and supplier email triage | Reading, sorting and drafting replies is slow for people and fast for AI | Sending without review. Start with drafts a person approves |
| Sales and CRM admin | Logging calls, updating records and preparing follow-ups is work nobody wants | Data quality. An agent writing to a messy CRM makes it messier, faster |
| Content and translation | First drafts plus human review are good enough, and today’s cost is visible | Brand, legal and regulated claims still need specialist review |
| Internal requests and approvals | IT, HR and procurement requests follow known paths with a lot of chasing | Anything involving personal information. Check privacy first |
| Reporting and board packs | Pulling data from several systems and summarising it is pure coordination | Figures the AI generates rather than retrieves. Trace every number to its source |
Where they don’t work (yet)
- Open-ended roles. “An agent to run customer service” isn’t a workflow. It’s a hundred workflows, most of which nobody has defined.
- High-stakes decisions with no review. Credit, hiring, individual pricing or anything that significantly affects someone’s rights needs a person accountable. New privacy obligations on automated decisions also start on 10 December 2026.
- Processes nobody has written down. If your team can’t describe the steps, an agent can’t follow them. Map the process first.
- Systems that can’t be connected. If software can’t read from or write to a system, the agent ends up working the screen like a person, and breaks just as often.
- Low-volume work. If it happens twice a month, the cost of building and monitoring it will outweigh the saving.
How to choose your first agentic workflow
- Start with cost, not technology. List the processes that consume the most hours or spend today. If you can’t put a number on the current cost, you can’t show a return.
- Pick something narrow and high-volume. One process, one team, a clear start and end. Hundreds of similar transactions a month beat a handful of complex ones.
- Check the systems and the data. Confirm every system in the workflow can be connected, and that the data it relies on is good enough to act on.
- Name an owner. One person accountable for the workflow once it’s live, who checks its output and decides when to change it.
- Decide where the human sits. Mark each step as automatic, reviewed or approved. Anything external, financial or hard to reverse starts as approved.
- Agree the measure and the deadline. Hours saved, cycle time, error rate or cost per transaction, measured against today’s baseline within 90 days of going live.
How to run it safely
An agent that takes actions needs tighter controls than a chatbot that only answers questions. The basics are simple:
- Least access. Give the workflow only the permissions it needs, through its own account rather than someone’s login.
- Approval gates. Payments, external emails, changes to customer records and anything irreversible need a person to approve them, at least until the workflow has a long record of getting them right.
- A log of every action. You should be able to see what the agent did, when and why.
- A way to stop it. Someone should be able to pause the workflow within minutes, and the process should still run manually without it.
- Testing on real examples. Run it alongside the current process before switching over, and keep checking it after launch.
Add each workflow to your AI register and put it through the same risk check as any other use of AI. My AI governance checklist for Australian boards sets out the questions to ask.
Cost and return
Licences are rarely the biggest cost. The bigger costs are mapping the process, connecting the systems, testing, and the time someone spends overseeing the workflow. AI usage, platform fees and monitoring carry on after launch, so put them in the business case too.
The return should be just as concrete: hours released, faster cycle times, fewer errors or a cost you no longer pay. The translation work paid off because the cost was already visible, so the saving was easy to see. If you can’t name the baseline, you’re not ready to fund the build.
My rule of thumb: if the first workflow can’t show a measurable return within a quarter of going live, either the scope was wrong or the process wasn’t ready. Fix that before funding a second one. I ask the same four questions of any AI initiative, and they’re in AI in the mid-market.
Who should own it
Agentic workflows cross teams, systems and risk boundaries, so they need a senior owner who can make decisions across all three. In many mid-market businesses, that’s the gap. It’s the work I do as a fractional Chief AI Officer: choosing the first workflows, getting them into production on tools such as n8n and UiPath, and putting the controls around them.
Where to start
To see whether your business is ready for agentic workflows, take my free AI Readiness Scorecard. It takes about five minutes and covers strategy, data, process, people and governance. Or book a 30-minute call and we’ll work out which of your processes would make a good first workflow.
Common questions
What's the difference between an AI agent and RPA?
RPA follows fixed rules. It clicks the same buttons in the same order every time, and breaks when something changes. An AI agent can read unstructured inputs, such as an email or a PDF, decide which step comes next and call the right tool. The best workflows usually combine the two, with rules handling the predictable steps and AI handling the ones that need reading and sorting.
Do we need to replace our systems to use agentic AI?
No. Most useful agentic workflows sit across the systems you already have, connecting your CRM, finance system, inbox and shared drives through their existing integrations. Replacing a core system to make room for AI agents is rarely the right first move.
How long does it take to get a first agentic workflow live?
For a narrow, well-defined process, about three months from choosing the use case to having it in production is a reasonable expectation, including a period running it alongside the existing process. If it's taking much longer, the process or the data usually isn't ready.
Will agentic AI replace staff?
In the mid-market it more often removes the chasing, copying and re-keying that fills people's days, so the same team can handle more work. Where roles do change, plan for it openly and early. Staff quietly work around a workflow they don't trust.
