This article explores the concept of an "agent harness," the essential software infrastructure that wraps around a Large Language Model (LLM) to enable autonomous, goal-directed behavior. While foundation models provide the core reasoning capabilities, the harness manages the orchestration loop, tool integration, memory, context management, state persistence, and error handling. The author breaks down the eleven critical components of a production-grade harness, drawing insights from industry leaders such as Anthropic, OpenAI, and LangChain. By comparing the harness to an operating system and the LLM to a CPU, the piece provides a technical framework for understanding how to move from simple demos to robust, production-ready AI agents.
* **Naive RAG:** Uses simple vector similarity for direct, fact-based queries.
* **Multimodal RAG:** Retrieves information across various formats, including text, images, and audio.
* **HyDE (Hypothetical Document Embeddings):** Generates a "fake" answer first to improve the retrieval of real documents.
* **Corrective RAG:** Verifies retrieved data against trusted sources to ensure accuracy.
* **Graph RAG:** Utilizes knowledge graphs to capture complex relationships between entities.
* **Hybrid RAG:** Combines vector-based retrieval with graph-based methods for richer context.
* **Adaptive RAG:** Dynamically switches between simple retrieval and complex reasoning based on the query.
* **Agentic RAG:** Employs AI agents to manage complex workflows involving multiple tools and sources.
* **Multimodal RAG:** Retrieves information across various formats, including text, images, and audio.
* **HyDE (Hypothetical Document Embeddings):** Generates a "fake" answer first to improve the retrieval of real documents.
* **Corrective RAG:** Verifies retrieved data against trusted sources to ensure accuracy.
* **Graph RAG:** Utilizes knowledge graphs to capture complex relationships between entities.
* **Hybrid RAG:** Combines vector-based retrieval with graph-based methods for richer context.
* **Adaptive RAG:** Dynamically switches between simple retrieval and complex reasoning based on the query.
* **Agentic RAG:** Employs AI agents to manage complex workflows involving multiple tools and sources.
