An AI agent is an LLM-powered system that can decide which actions to take, execute them (via tools and APIs), observe results, and iterate until a goal is achieved — all without human intervention at each step.
Core Components
LLM Core
The reasoning engine. Decides what to do next, formats tool calls, and synthesises final answers. Needs strong instruction-following ability.
Tools / Functions
Structured actions the LLM can invoke: web search, code execution, database queries, file I/O, calendar APIs. Defined via JSON schemas (OpenAI Function Calling format).
Memory
Short-term (context window), episodic (vector store of past interactions), and semantic (RAG knowledge base). Lets the agent recall previous steps and known facts.
Orchestrator
Frameworks like LangGraph, CrewAI, AutoGen, or custom loops that implement the planning cycle: observe → reason → act → repeat.
Common Agent Patterns
ReAct (Reason + Act)
Alternates between thinking (chain-of-thought) and acting (tool call). Most common single-agent pattern.
Plan-and-Execute
Planner model decomposes the goal into sub-tasks; executor model completes each sub-task. Faster for complex, predictable workflows.
Multi-Agent
Specialised agents (researcher, coder, reviewer) collaborate. CrewAI and AutoGen are popular frameworks for this pattern.
Why It Matters for On-Premise
Cloud agentic platforms (OpenAI Assistants, Vertex AI Agent Builder) send all tool inputs and outputs to vendor servers. If your agent is querying internal databases, reading HR files, or executing code against production systems, every intermediate step is a data leakage vector. Running the LLM core on-premise with local tool connectors ensures the entire reasoning trace stays inside your network boundary.