Constructing Context and Semantics for AI Agents
Published by Self-published · Tech
A practical guide to embeddings, knowledge graphs, memory systems, RAG, evaluation, and production agent architectures.
Who this book is for
AI and ML engineers building agent systems, software engineers and architects moving into AI product development, and technical leaders, product managers, and decision-makers evaluating AI agents for their organizations.
Table of contents
- Introduction: AI Agents and Context Overview
- LLM Fundamentals and Context Windows
- Prompt Engineering vs. Context Engineering
- Semantic Representations
- Knowledge Graphs and Structured Context
- Memory Architectures for Agents
- Multimodal Context and Grounding
- Agent Frameworks and Tools
- Evaluation of AI Agents
- Safety, Ethics, and Bias Mitigation
- Best Practices and Patterns
Excerpt
A customer support bot that forgets the user's name mid-conversation. A coding assistant that ignores the project's authentication layer and writes a second one from scratch. A data analyst agent that fabricates statistics because it never received the actual dataset. These failures share a common root cause: the agent lacked the right context at the right time. Context is not a nice-to-have feature for AI agents. It is the foundation on which every useful behavior depends. An agent's ability to reason, plan, and act correctly is bounded by the information it can access during each decision. When that information is incomplete, stale, or poorly structured, the agent produces outputs that range from mildly unhelpful to dangerously wrong.
