AI Agent Frameworks

I see four key agentic design patterns emerging: reflection (where agents review their own output), tool use (where agents call external APIs), planning (where agents break tasks into subtasks), and multi-agent collaboration. Each of these patterns, applied even to a mediocre base model, can dramatically improve output quality.

We have mapped 1,200+ enterprise AI companies across 47 categories. The fastest-growing segments are: AI agents for operations (340% YoY growth in funding), code generation platforms (280% YoY), and AI governance tools (250% YoY). Notably, the "AI wrapper" category that critics dismissed is now producing companies with $50M+ ARR.

The agent paradigm is fundamentally different from the chatbot paradigm. When you give an AI system the ability to use tools, browse the web, write code, and interact with APIs, you are not just building a smarter chatbot -- you are building a digital worker. The companies that understand this distinction earliest will have a massive structural advantage.

AI agents are the new aggregators. Just as Google aggregated web content and Facebook aggregated social connections, AI agents will aggregate services and workflows. The company that owns the agent layer -- the interface between the user and all the services they use -- captures the majority of the value. This is why every platform company is racing to be the default agent.

I propose a five-level RAG maturity model: Level 1 is basic retrieval with a vector store. Level 2 adds hybrid search and reranking. Level 3 introduces agentic RAG where the system decides what to retrieve. Level 4 adds multi-step reasoning over retrieved content. Level 5 is self-improving RAG that learns from user feedback.