Context Engineering: The Missing Piece in Building AI Agents That Actually Work

#ai #llm #rag

Introduction

If you're building with LLMs - chatbots, workflow engines, AI-powered verifiers, you've probably hit that wall: your agent gets dumber as the conversation gets longer. I noticed this myself, especially when experimenting with retrieval-augmented generation (RAG) chatbots. After a while, the bot would forget what it was supposed to be doing and drift back to acting like a plain language model all over again. At first, I blamed my prompts. Maybe the instructions were too vague? Maybe I missed an if-else or some crucial line in the system message?

But deep down, I sensed the problem was bigger. No single prompt could capture the messy, evolving reality of a multi-step task or a meaningful back-and-forth. When working with LLMs, every new message, tool call, or background instruction matters just as much as that first prompt. The system prompt is just step one. What really shapes agent behavior is what you feed the model at every turn.

Recently, reading Anthropic’s “Effective Context Engineering for AI Agents” gave me the language to describe what I’d always sensed but hadn’t crystallized: context engineering. Instead of seeing the prompt as your only lever, it’s about curating the right slice of information at each stage of the workflow.

Suddenly, those half-forgotten intuitions clicked. If your agent is handling multi-step support tickets, processing documents, or coordinating any complex task, it’s the accumulated context, not just a one-time prompt that determines its performance. The trick isn’t clever prompting; it’s building a system for managing context, and that’s what I want to break down in this article.

From Prompts to Context – The Engineering Shift

Prompt engineering is the art of writing static instructions for a model. You give it a “system prompt” and a user query, handed off once at the start. This approach works well for simple, single-turn tasks.

Context engineering goes further. It’s a dynamic process of curating what enters the model’s context window at every step: system messages, relevant documents, outputs from tools, and slices of conversation history. With each turn, you actively decide what’s important, trim what’s not, and ensure the agent has exactly what it needs for the current moment.

About the image: In contrast to the discrete task of writing a prompt, context engineering is iterative and the curation phase happens each time we decide what to pass to the model (Image from Anthropic's Article)

This shift powers agents that stay coherent, relevant, and goal-driven even when tasks get complex or lengthy.

About the image: At one end of the spectrum, we see brittle if-else hardcoded prompts, and at the other end we see prompts that are overly general or falsely assume shared context. (Image from Anthropic's article)

This spectrum shows how effective system prompts land between being too rigid and too vague, finding the “just right” zone gives your agent clear, useful guidance.

It’s still important to craft the right system prompts. Anthropic’s research shows there’s a ‘just right’ zone between being too vague and too rigid.

Attention Budget – Why Less Is More

A key insight behind context engineering is that LLMs have a finite attention span. Every token you add to context competes for focus, and too much clutter causes “context rot”. The agent then forgets, mixes up details, or derails.

Transformers, the backbone of modern LLMs, make every token attend to every other. However, performance drops as context windows expand. The solution isn’t to add more data. It’s about making smarter selections. High-performing agents use only the smallest, most relevant set of tokens at each step.

Engineers should treat context as a scarce resource. Prioritize clarity and relevance, constantly trim noise, and never overload your agent’s working memory.

What Builders Should Do Differently

For developers and students, context engineering means rethinking how you build and manage AI workflows:

Architect your system to dynamically update context at each turn. Don’t rely on a massive initial prompt.
Summarize, compress, or discard information as the task progresses. Keep your agent’s focus sharp.
Design your tools to return lean results. Avoid verbose dumps and provide just actionable data.
Build a “context management” layer in your code, not just a clever prompt handler.

The practical payoff? Agents that are reliable, adaptable, and capable of handling real-world complexity. The future of AI workflows isn’t just about clever instructions; it’s about ongoing, intentional context curation.

Acknowledgements

This article draws extensively from the research and insights shared by Anthropic’s Applied AI team. I would like to acknowledge Prithvi Rajasekaran, Ethan Dixon, Carly Ryan, and their collaborators for their significant contributions to the field of context engineering. For a comprehensive exploration, please refer to the original publication: Effective context engineering for AI agents, Anthropic.