Building AI Agents

The ReAct (Reason + Act) pattern is the primary breakthrough in agentic AI. Instead of jumping to an answer, the model is prompted to generate an internal 'Thought' followed by an 'Action.' After the action is executed by your code, the model 'Observes' the result. This iterative process allows the AI to self-correct, refine its strategy, and handle unexpected errors in real-time.

An agent is only as good as its Tools. Designing a tool requires creating a clear, natural language description of its purpose. The LLM reads these descriptions to 'match' the user's intent to a capability. If you provide a tool called 'WeatherLookup' without describing that it can provide 'Current temperature and conditions,' the model may never realize it should use it for a question like 'Is it raining in London?'

Advanced agents are capable of Goal Decomposition. When given a high-level goal (e.g., 'Conduct a competitive analysis of three EV companies'), the agent breaks it down into sub-tasks (search, summarize, compare). It then executes each task sequentially, maintaining a 'working memory' of its findings until the final objective is met. This level of autonomy is the foundation of the next generation of productivity software.