AI Systems

A multi-agent system is useful when separate agents contribute different information, tools or independent verification. It is wasteful when several models repeat the same analysis under different names.

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.

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.

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.

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.

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.