Agentic Workflow

An agentic workflow replaces a single prompt with a loop that plans, uses tools, reads results, and continues.

Ask a standard large language model to write a complex application and it tries to emit the whole codebase in one linear dump. Context fills up. Logic breaks. An agentic loop parses the goal, writes a plan, produces a small piece of code, runs it in a terminal, reads the error log, looks up a fix with a web search tool, and tries again. The product is closer to a digital worker than a chat box.

Common patterns include multi-agent setups where separate models act as coder, reviewer, and tester and argue over the work, and structured reflection where the model is told to critique intermediate outputs before it finalizes them. Surrounding an average LLM with a tool-equipped loop often beats one giant prompt to a larger model with no tools.

The gain comes from iteration and evidence, not from hoping a single pass is enough.

The loop is observe, plan, act, observe again. Tools include a terminal, a browser, and sometimes extra models. Multi-agent designs assign coder, reviewer, and tester roles so one transcript contains disagreement, not a single unchallenged dump. Reflection prompts force a critique of the current patch before the next edit.

That structure pulls extra performance out of a mid-size LLM because errors in logs become evidence. A one-shot prompt to a larger model still cannot run the compiler or read the stack trace. An agentic workflow is that loop plus your tools and stop rules. LangGraph, Temporal, and similar systems schedule those steps.