Agentic Workflow

An agentic workflow shifts AI interactions from single-shot prompts to iterative, autonomous problem-solving loops. When you ask a standard large language model to write a complex software application, it attempts to generate the entire codebase in one linear output, inevitably failing as context windows overflow and logic breaks. An agentic workflow changes the architecture.

The AI operates as a continuous loop: it parses the goal, breaks it down into a plan, writes a small piece of code, uses a terminal tool to execute it, reads the error logs, reflects on its mistakes, researches a fix via a web search tool, and iterates. It replaces the 'chat assistant' model with a 'digital worker' model.

Common design patterns for agentic systems include multi-agent collaboration - where specialized AI models act as coders, reviewers, and testers debating each other - and structured reflection where the model is explicitly prompted to critique its own intermediate outputs before finalizing them.

This workflow pulls strong performance improvements out of existing models, proving that surrounding an average LLM with a highly structured, tool-equipped cognitive loop yields significantly better results than simply prompting a massive model and hoping for the best.