Master Context Engineering to build better AI Agents
Discover the secret behind reliable, next-gen AI. Context Engineering is the new topic in AI, and every agent builder should learn about it today.
"Context engineering is the delicate art and science of filling the context window with just the right information for the next step." - Andrej Karpathy
Think of context engineering like setting up a workspace for a chef. The chef (the AI) can cook better if you give it the right tools, ingredients, and recipe instructions. Context engineering is about ensuring the AI has all the necessary information and resources at the right time to perform its job effectively.
Why is Context important?
Imagine you’re trying to help a friend, but you only hear half of what they say, or you forget what they told you earlier. You’d probably give bad advice. AI agents have the same problem. If they don’t have the right background information, they make mistakes. Good context means the AI understands what’s going on and can help you better.
What goes into “Context”?
Here’s what the AI needs to work well, just like a chef needs more than just a stove:
System Instructions: The rules or guidelines for how the AI should behave.
User Input: What you just said or asked.
Conversation History: What you and the AI have already talked about.
Long-Term Memory: Things the AI remembers from past chats.
Retrieved Information: Facts or data that the AI looks up from books, the internet, or databases.
Tools & Functions: Special gadgets or apps the AI can use, like a calculator.
Output Formats: The way the AI should give you its answer (like a list, table, or message).
Context Engineering vs. Prompt Engineering
Prompt Engineering: Like writing a single, clear instruction for the AI.
Context Engineering: Like making sure the AI has all the background info, tools, and reminders it needs, so it can do its job properly, not just follow one instruction.
How do you build good context?
1. Dynamic Context Assembly - Use pipelines to gather and organize system instructions, user data, conversation history, and external knowledge before each agent action.
2. Retrieval-Augmented Generation (RAG) - Integrate semantic search and vector databases to pull in relevant documents or facts on demand, grounding agent responses in real data.
3. Memory Management - Maintain both short-term (session) and long-term (persistent) memory, summarizing or compressing as needed to fit within context window limits.
4. Tool and API Orchestration - Clearly define available tools and their schemas, and dynamically select the right tool for each task.
5. Context Compression & Prioritization - Rank and filter information by relevance, recency, and importance to avoid overloading the context window.
6. Validation & Governance - Implement quality checks, context validation, and quarantine mechanisms to prevent context poisoning and ensure consistency across agent workflows.
Common Problems (and Simple Fixes)
Context is the fuel that powers intelligent behavior in AI agents. But just like bad fuel can stall a high-performance engine, poor context design can derail even the most advanced workflows. Here are six of the most common pitfalls we see in the field, and how to engineer around them with clarity and precision.
𝟏. Adding too much context
More data ≠ better output.
Overloading the context window makes the model noisy and distracted.
👉 Fix: Filter aggressively. Only load what’s relevant for the current step.
𝟐. Ignoring compression
Token limits are real. Raw, bloated text wastes precious space.
👉 Fix: Add a compression layer. Summarize, distill, trim. Think like a systems engineer.
𝟑. Mixing unrelated contexts
Blending tools, tasks, or topics confuses the model.
👉 Fix: Scope context by task. Keep workflows logically isolated.
𝟒. No context lifecycle management
Outdated memory is worse than none. Most teams just log everything forever.
👉 Fix: Use versioned context stores. Add expiry, cleanup, and TTL policies.
𝟓. Treating all contexts equally
If everything is important, nothing is.
👉 Fix: Rank inputs by relevance. Prioritize high-impact details.
𝟔. Sending raw, unfiltered data
Dumping messy content into prompts kills clarity and reasoning.
👉 Fix: Preprocess before injection. Structure with intent and format for clarity.
Emerging Trends
As agent workflows grow more sophisticated, so do the ways we manage their context. Manual context shaping is quickly becoming unsustainable, especially in dynamic, real-time environments. That’s why we’re seeing a wave of innovation focused on automating and scaling context for smarter, safer agents.
Automated Context Pipelines are gaining traction, systems that dynamically pull, clean, and inject only the most relevant data into prompts, based on task and user. No more handcrafting every context window.
Context-as-a-Service is emerging as a new category: external platforms that manage retrieval, memory, ranking, and even compliance, so you can focus on business logic.
Multimodal Context is expanding fast, and AI agents are learning to reason not just from text, but also from images, audio, and video, making them more perceptive and grounded.
Ethics & Security are becoming critical pillars of context design. Agents must be protected from data poisoning, privacy leaks, and hallucinations by enforcing robust governance and validation layers.
The future of agent reliability starts with how we handle context. This is no longer a nice-to-have; it’s infrastructure.
How can you learn more?
Context Engineering is still a fast-evolving discipline, and staying sharp means going beyond frameworks and diving into real techniques. Whether you're just starting out or already building multi-agent workflows, there’s no shortage of resources to level up.
Below is a curated list of guides, tutorials, and blogs to help you master the art (and science) of designing smart, reliable agent context.
Intro Guide: DataCamp: Context Engineering
Practical Tutorial: YT Playlist: Context Engineering
Handbook: GitHub: Context Engineering Handbook
Advanced Guide: Context Engineer.org
Blog/Newsletter: LangChain Blog, LlamaIndex Blog
Bottom Line:
Context engineering is about making sure your AI has everything it needs to be helpful and smart. The better the info and tools you give it, the better it will work for you.
Don’t forget to register for upcoming webinars.
In the AgentBuild webinars, we break down the core concepts behind building effective AI agents and agentic workflows. Each session goes beyond theory; we share real-world insights into the challenges organizations face and equip agent builders with practical strategies, patterns, and best practices to succeed.
Please join us. Schedule below.
19.07.2025 09:00 AM GMT - Multi-agent Systems [👉Register]
02.08.2025 09:00 AM GMT - Real-world Workflows [👉Register]
16.08.2025 09:00 AM GMT - Evaluating Agent Performance [👉Register]
30.08.2025 09:00 AM GMT - Hosting and Sharing AI Agents [👉Register]
Stay curious, keep building, and watch your agents come to life like never before.
Thanks,
Sandipan.
AgentBuild Community