Final Takeaway

An LLM agent is an engineered system made of identifiable layers: a stateless model, a context manager, an output parser, a control loop, tools, guardrails, and observability. Each layer has a specific job, and the overall behavior comes from how the layers work together, not from any autonomy the model has on its own.

The model provides probabilistic reasoning. The harness provides state, control, safety, memory, and observability. The tools provide action. The guardrails provide boundaries. The traces provide understanding.

When designing an agent, build from the bottom up:

1. Understand the stateless model.
2. Manage context as a finite resource.
3. Define structured output contracts.
4. Expose controlled tools.
5. Wrap everything in a deterministic loop.
6. Add memory only as needed.
7. Enforce guardrails outside the model.
8. Make every run observable.

This eight-step sequence is the core architecture that every agent system, regardless of complexity, must address. Other patterns, frameworks, and design choices are variations on these fundamentals.