Agents

Most LLM applications start simple: one prompt, one response, ship it. Then requirements grow. The task needs to search the web, then read the results, then decide whether to search again, then synthesize everything. You add more logic. Then you need one agent to write a plan and another to execute it. Suddenly you are managing state, routing decisions, and failure modes across multiple LLM calls, and a simple chain is not the right abstraction anymore. ...

Everyone knows to tell the model to “think step by step.” That was 2022. In 2026, the basics are table stakes, and the developers extracting the most value from LLMs are using techniques that go well beyond the starter guides. This article covers what actually works: the patterns that experienced LLM engineers use in production, the failure modes they have learned to avoid, and the reasoning behind why these techniques work rather than just a list of tips to copy. ...