The LLM agent build guide (www.vellum.ai)

This guide is a practical playbook for building reliable LLM agents, walking teams through memory, context engineering, tool integration, architecture patterns, and end-to-end testing. It responds to a clear industry pain point—MIT found ~95% of GenAI pilots never hit production—by offering concrete patterns and guardrails that make agents useful rather than brittle. The guide situates LLM agents amid rapid market and adoption growth (LLM market projected from $5.6B in 2024 to $36.1B by 2030; McKinsey/IBM surveys show fast adoption and near-universal developer interest), arguing that better design choices will determine which pilots scale. Technically, the guide distills agents into four pillars: model, memory, context, and tools. Memory is split into short-term (per-call context) and long-term (episodic, semantic/vector stores, and user-specific profiles) with practical management tips—distill logs, apply retention/consent policies, and commit critical state to long-term stores. Context engineering focuses on exposing minimal, relevant slices of state to each step or subagent to reduce token costs and drift. For tool integration it contrasts function calling (model emits JSON to instruct your backend to run a function—good for a few custom hooks) versus MCP (Model Context Protocol), a standardized schema for describing tools that scales across agents, versions, auth layers, and models. The guide also outlines architecture choices from simple single-agent loops to coordinated multi-agent systems and emphasizes testing, normalization of tool outputs, and retrieval strategies for production reliability.