The Architecture of Agentic AI: Building Autonomous Systems That Think, Plan, and Act
Agentic systems pursue objectives instead of answering prompts, which turns an AI problem into a systems-engineering one. This article walks the architecture around the model through three disciplines — the harness (what the agent can see and touch), the loop (how it learns what actually happened) and the graph (how work flows) — and shows where each one is implemented in the system behind this site.
- Agentic AI
- Architecture
- Orchestration
- Guardrails
- Evaluation
What it covers
- An answer is not an action
- Why a model alone is not an agent
- Start with a mission, not a prompt
- Discipline 1 — The harness: what the agent can see and touch
- Discipline 2 — The loop: how the system learns what happened
- Discipline 3 — The graph: how work flows
- The whole picture
- Production reality: sufficient reasoning, not maximum reasoning
+ 4 more sections