The direct answer
An agentic workflow is a way of using a language model where it doesn't just answer — it works. The model makes a plan, takes an action using a tool (a web search, a database query, a code runner), observes what comes back, and uses that new information to decide its next step. It repeats this plan → act → observe loop until the task is finished or it gets stuck. That loop is what turns a chatbot into something that can do multi-step jobs in the real world.
The pattern has a name: ReAct, for “reasoning + acting,” introduced by researchers at Princeton and Google in a 2022 paper. Their insight was that thinking and doing reinforce each other. Reasoning alone can drift into confident nonsense; acting alone can't plan. Interleaved — think a little, do a little, check what happened, repeat — the model grounds its plans in real feedback and recovers from mistakes along the way.
How it works
Picture the loop running. A user asks an assistant to research a topic and draft a summary. The model first reasons: “I need sources on this topic — I'll search the web.” It acts: calls the search tool. It observes: a list of results. Back to reasoning: “These two look relevant, but one contradicts the other — I'll open both and compare.” Another action, another observation, and so on until it has enough to write the summary. Every tool result becomes fresh evidence the model can reason about, which is why agents hallucinate less than models answering from memory alone.
Under the hood, this is just a language model, some tools, and a loop with a stopping condition — “I'm done,” or a maximum number of steps. What makes it powerful is that the path isn't fixed in advance: the model decides each next step based on what it just saw. That flexibility is the whole point — and, as Anthropic's engineering team has argued, it should be used sparingly, because it trades speed and cost for capability.
A simple example
An illustration.
Imagine asking a friend to plan a weekend trip. The non-agentic approach is them guessing an itinerary from memory. The agentic approach is them checking flight prices, noticing the Saturday flight is sold out, checking Sunday instead, looking up the weather, and adjusting the plan as each answer comes in. The loop — check, see, adjust — is the agentic workflow.
As an illustration of the loop, not a real itinerary.
Why it matters
This loop is the engine under nearly everything now called an “AI agent”: coding assistants that write, run, and fix code; research tools that search, read, and synthesize; support bots that look up your account and take actions. Whenever a task needs fresh information, has unknown steps, or requires trying things and seeing what happens, a one-shot answer isn't enough — you need the loop.
It also explains the current gold rush honestly. Most production “agent” systems aren't mysterious; they're simple, composable patterns — a chain of steps, a router that picks a specialist, one model checking another's work in a loop. Anthropic's widely-cited guidance puts it bluntly: start with the simplest thing that works and add agentic complexity only when it demonstrably helps. The loop is a tool, not a virtue.
The common misunderstanding
The common misunderstanding is that “agentic” means smarter, and that every task should be agentic. In reality, Anthropic draws a sharp line: a workflow is a path a developer wrote in code in advance (step 1, then step 2, then step 3); an agent is a path the model chooses at runtime, loop by loop. Fixed paths are predictable, cheap, and testable — and for most well-defined jobs, they're the right answer. Agents earn their keep only on open-ended tasks where you can't predict the steps. Calling a fixed recipe “agentic” doesn't make it more capable; it just makes it harder to debug.
What changed recently
The ReAct paper landed in 2022 as a prompting trick; Anthropic's engineering team later documented which of these patterns actually survived contact with production, and the vocabulary — chaining, routing, orchestrator-workers, evaluator-optimizer — became the industry's shared language. Meanwhile agents moved from demos into real tools: coding agents that fix real bugs, computer-use systems that click through real software. The open questions now are practical ones: how to make the loop reliable, how to bound its cost and its mistakes, and how much human oversight each loop needs.
Try it on PlainLogic
Agent Mission is PlainLogic's game about the loop itself — plan your moves, use your tools, and watch what the environment throws back.