Agentic AI systems are fundamentally different from standard LLM applications. Where a chatbot responds to a single prompt, an agent perceives its environment, plans a sequence of actions, uses tools, and iterates toward a goal — often over minutes or hours.
The Five Components of an Enterprise Agent
Every production agent needs: (1) a reasoning engine (LLM), (2) memory (short-term context + long-term vector store), (3) tools (APIs, databases, code execution), (4) a planning mechanism, and (5) a feedback loop. Missing any of these creates a fragile system that fails in production.
Memory Architecture for Enterprise Agents
Enterprise agents need three memory types: working memory (current task context, ~100K tokens), episodic memory (past task outcomes in a vector database), and semantic memory (enterprise knowledge — policies, procedures, domain facts in a knowledge graph).
