automation is not a new concept. but for years, it has been largely confined to simple, repetitive tasks that can be defined in a linear script. if x, then y. but what about more complex processes? what about tasks that require reasoning, planning, and adaptation? this is where agentic workflows come in.
From Scripts to Goals
an agentic workflow is a system where you give an ai agent a high-level goal, not a set of instructions. for example, instead of writing a script to "check for new emails, extract the invoice number, find the corresponding po in the database, and approve the payment," you give an agent the goal: "process incoming invoices." the agent then has to figure out the steps. it might use a "read_email" tool, then a "parse_document" tool, then a "query_database" tool, and finally a "submit_for_approval" tool. if one step fails, it can reason about what went wrong and try a different approach. this is a fundamental shift from instruction-based automation to goal-based automation.
it’s like having a team of infinitely scalable, perfectly reliable junior employees who can be trained to handle complex operational tasks.
agentic workflows represent the next frontier of business process automation. by combining the reasoning power of llms with a well-defined set of tools, we can automate a whole new class of complex tasks, freeing up human workers to focus on strategy, creativity, and high-level decision-making.