Context Engineering: Building the Brain Around Your AI

If you're building agentic applications, or even just starting out, you've probably hit this wall: Your AI agent gets stuck in an infinite loop, calling the same tool over and over, even though your tool definitions are perfect.

This happened to me while building a manufacturing intelligence chatbot. The agent had access to dozens of tools - inventory APIs, sensor data, quality metrics, maintenance logs. Ask it "What's causing our delivery delays?" and here's what would happen:

It calls the inventory checking tool. Gets the data. Then calls the same inventory tool again with identical parameters. Then tries the inventory status tool. Then back to the inventory checker. Five minutes later, it's still obsessing over inventory when the real problem was a machine breakdown logged in the maintenance system - a tool it never even tried to touch.

Why does this happen when tool definitions are perfect?

I'm sure you've faced the same mystery. When you have lots of tools, their descriptions overlap. The agent isn't confused about what each tool does - it's confused about which one to pick.

But here's what changed everything: Once you get the context right, everything else - accuracy, reliability, even hallucination control - falls into place.

The solution wasn't fixing the tools. It was engineering the entire context around them. And tool management is just the beginning of what context engineering can do.

The Context Engineering Revolution

While developers have been managing prompts, RAG systems, and memory, calling it "context engineering" and treating it as a distinct field, it only started gaining traction recently.

Recently, the AI community has started recognizing "context engineering" as a distinct discipline - and industry leaders aren't holding back.

Aaron Levie, CEO of Box: "Context engineering is increasingly the most critical component for building effective AI Agents." He calls it "the long pole in the tent" - the real bottleneck in AI deployment.

Andrei Karpathy (former Tesla AI director, OpenAI founding member) endorsement went deeper:

So What Exactly Is Context Engineering?

Think of context engineering as the difference between giving someone directions versus giving them a GPS with real-time traffic, weather alerts, and local knowledge built in.

• Prompt engineering: "What's the best way to phrase my question?"

• Context engineering: "What does the AI need to know to answer correctly?"

The Context Window: Your AI's Working Memory

Every LLM has a context window - think of it as the AI's RAM. It's everything the model can "see" when making a decision. Context engineering is about architecting what goes into that window, making sure every token earns its place.

Here's what actually makes up the context:

1. System Instructions - Sets the AI's role, behavior, and guardrails
"You are a fintech support assistant for company ABC. Always prioritize user clarity and accuracy."

2. Memory Layers

• Short-term (Conversation History): Messages within the current thread

• Long-term (Persistent Memory): Summaries of past interactions, user preferences, profile info, or chats from different threads

3. External Information Retrieval

• RAG-style document lookup: Pulling data from PDFs, code snippets, or videos

• Third-party integrations: Real-time data like billing information or API responses

4. Tool Definitions

• How to call available tools (APIs)

• How to process and use tool outputs in next steps

• Dynamically selected based on task relevance

5. User Prompt

• The direct instruction from the user

• The immediate query that triggers the response

6. Output Constraints

• Desired format specifications

• Structure requirements for the response

Proven Patterns for Context Engineering

1. Dynamic Tool Selection

Use semantic search (RAG techniques) to load only relevant tools per query instead of all tools at once. Research shows beyond 30 tools; descriptions overlap causing confusion. Result: 44% performance improvement, 3x better tool selection accuracy.

2. Context Compression

Models suffer from "lost in the middle" - forgetting information buried between start and end. Techniques like the Sentinel framework use attention-based filtering to achieve 5x context reduction while maintaining performance.

3. Context Isolation

Break tasks into smaller, isolated jobs with their own context windows. Anthropic's multi-agent research systems with separate contexts outperformed single-agent systems by 90.2% on their internal eval.

4. Memory Management

• Summarization: When context grows beyond 100k tokens, agents favor repeating past actions over novel solutions - summarize to prevent this

• Offloading: Anthropic's "think" tool (scratchpad) for storing notes outside main context yields up to 54% improvement for specialized agents

The Bottom Line

Context engineering isn't about making your model smarter - it's about making its environment smarter. It's the discipline of giving your AI exactly what it needs to succeed, nothing more, nothing less. Every token in the context influences the model's behavior. Context is not free. Make each token count.

Disclaimer: The tools and opinions shared in this post are based on general observations and should be considered as suggestions rather than endorsements. Individual experiences may vary.