An agentic workflow is a multi-step process in which one or more AI agents do the work, deciding how to handle each step, using tools and checking results, rather than following a fixed script. It usually has defined stages, handovers between agents or tools, and points where a person approves the output before it moves on.
How an agentic workflow works
Traditional workflow automation follows rules exactly: when a form is submitted, add the contact to a list and send email two. An agentic workflow sits between that and a single AI prompt. The overall route is designed by a person, but inside each stage an AI model makes decisions the rules could not.
A weekly paid ads report is a good example:
- One step pulls spend, clicks and conversions from Google Ads and Meta, and enquiries from the CRM.
- An agent compares the figures with the previous four weeks and flags anything unusual, such as a campaign whose cost per lead has doubled.
- For each flag, it looks for likely causes: a new search term, a paused ad, a tracking gap.
- A second agent drafts a plain-English summary, and a reviewing step checks the numbers in the draft against the source data.
- A person reads the summary and decides what to change.
Common patterns include a planner that breaks a task down and hands parts to specialist workers, a reviewer that checks another agent’s output, and a router that sends each item down a different path. Connections to tools are often made through standards such as the Model Context Protocol or automation platforms such as Zapier and Make.
Why it matters
Much marketing work is a chain of small judgements on top of routine data handling: categorising search terms, checking landing pages, enriching leads, preparing briefs. An agentic workflow can handle the routine parts and the simpler judgements, leaving people to make the decisions that need business knowledge.
For a small UK business without an in-house analyst, that can turn a half-day task into a short review. The trade-off is reliability. Each AI step can make a mistake, and errors compound across a chain, so a five-step workflow that is right most of the time at each step can still be wrong more often than you would accept overall.
Common mistakes
- Automating a process nobody has written down, so the workflow reproduces the confusion faster.
- Using agents for steps a simple rule would handle more cheaply and predictably.
- No human approval before anything reaches customers, ad budgets or the live site.
- No logging, so when an output is wrong nobody can see which step went astray.
- Ignoring running costs. Each agent step uses paid model calls, and long chains run every day add up.
How to act on it
Write the process out as numbered steps first, as if training a new assistant. Mark each step as rule-based, needing judgement, or needing a decision only you can make. Automate the rule-based steps with ordinary automation, use an AI model only for the judgement steps, and keep your own decisions as approval points.
Run the workflow alongside your existing process for a few weeks and compare the outputs before you rely on it. If you want an outside view on which parts of your marketing are worth automating this way, I cover it in digital marketing strategy and consulting.