An agentic workflow is an automated process where an AI agent decides what to do at each step, rather than running a path you fixed in advance. You give it a goal and a set of tools, and it reasons about which tool to use, in what order, until the job is done.
Picture a junior assistant instead of a vending machine. A vending machine only ever does the one thing for the button you press. An assistant can read an email, notice it needs a refund, check the order, draft a reply and flag the odd cases for you. The “brain” making those calls is usually an LLM, wired into a wider AI orchestration layer that gives it memory and access to your real systems.
A concrete shape helps. Say a new invoice lands in a shared inbox: the agent reads it, pulls the matching purchase order from your accounting tool, checks the totals line up, and either books it or, when something does not match, writes a short note to a human and waits. A fixed script would choke on the first invoice that did not look like the template. The agent just adapts.
This flexibility is the appeal and the catch. An agent can handle messy, varied work that a rigid script never could. But it can also make a wrong call, so good agentic workflows keep tight guardrails: limited permissions, logging of every decision, and a human checkpoint before anything irreversible happens. Skip those and a single confident mistake can ripple through dozens of records before anyone notices.
At TopDevs we build agentic workflows for the parts of a client’s process that are too varied for fixed rules, while wrapping each agent in clear limits so it stays useful and never goes off the rails.
