ML//agent//agentic system

An agentic system is the complete arrangement that allows model generations to pursue a task over time. It includes the model, instructions, context assembly, memory (agent memory), tools, permissions, execution environment, stopping rules and whatever evaluates success (often a verifier such as a test suite). Its runtime core is the agent harness, which connects the model to state, tools and enforcement.

An agentic system is the complete arrangement that allows model generations to pursue a task over time. It includes the model, instructions, context assembly, memory (agent memory), tools, permissions, execution environment, stopping rules and whatever evaluates success (often a verifier such as a test suite). Its runtime core is the agent harness, which connects the model to state, tools and enforcement.

This wider unit matters because blaming the model is often an incomplete diagnosis. A misleading tool result, excessive permission, stale memory or badly chosen timeout can produce failure without any change to the model weights. Likewise, the same model can look far more capable when the surrounding system gives it better observations and actions, more time to think (test-time compute) and a way to check its work.

The model proposes. The system remembers, executes and contains.