AI Systems

ReAct interleaves reasoning, allowed actions and observations so an agent can verify evidence, recover from errors and stop when its task is complete.

An agent can be given a larger system prompt every time its responsibilities grow. That approach works until instructions become difficult to discover, maintain and test. Agent Skills provide a more modular option: package procedures, scripts and references as capabilities that can be loaded when relevant.

Tools turn an AI system from a text generator into an actor. They also create the point where a probabilistic decision meets a deterministic API and, potentially, the real world.

The most capable architecture is not automatically the best architecture. A fixed workflow, one tool-using agent and a multi-agent system trade predictability for flexibility at different rates.

Adding an “Approve” button does not automatically make an AI workflow safe. Human review works only when the reviewer has the information, time and authority to detect a meaningful problem before the action occurs.

A coding agent is not a model that produces a large code block. It is a tool-using system that can inspect a repository, edit files, run commands, observe failures and repeat the cycle until an external check says the change works.