The Agentic Loop: Perceive → Plan → Act → Observe
An AI Agent is not just a larger language model. It is an agentic architecture that wraps around a foundation model to create an autonomous feedback loop:
Instead of generating a text response and immediately stopping, the agent decides whether it needs more information, issues commands to tools (such as web search, file readers, or database queries), inspects the returned results, and determines the next step until the user's objective is fulfilled.
1. The Brain
The reasoning model (e.g. Gemini 2.5 Pro or Claude 3.7) provides instruction-following, hypothesis generation, and intent interpretation.
2. Planning
Breaks large objectives into distinct sub-tasks, estimating dependencies and tracking completion status.
3. Tools & MCP
Uses standardized protocol connections to run shell scripts, query vector stores, trigger GitHub workflows, and call web APIs.
4. Reflection
Observes tool output, evaluates errors, and self-corrects without requiring the human user to rewrite the initial instructions.
Detailed Guide & Comparison
Understand how AI Agents compare directly against raw Generative AI models, including side-by-side architecture tables, customer service walkthroughs, and key implementation takeaways: