A Guide to Context Engineering: Setting Agents Up for Success

There’s been a lot of talk about context engineering recently and at first glance it sounds like just another Silicon Valley buzzword. We had prompt engineering which sounded like a fancy way of telling an AI what it should do, and now context engineering which sounds like an even fancier way of saying prompt engineering.

But it’s more than that. In my guide on how to design AI agents, I mentioned that some of the core components are Instructions, Tools, and Memory.

Instructions here are the prompt you give to the LLM where you tell it what it should do, how it should behave, and so on. This is where prompt engineering comes in handy.

But a truly autonomous agent needs more than just a set of instructions. Think about when you hire a human, let’s say an executive assistant. Your instructions to them might be to answer emails, manage your calendar, and maybe even guidelines on how to handle communications.

But the assistant needs more than just that. If someone sends you an email asking to meet, the assistant needs to know who that is, what your history is with that person, and whether it’s worth your time meeting with them, before responding.

They need context.

The same principle applies to building autonomous agents. We need to dynamically supply the right context to our agent so that it may determine the best path forward. And we do this using Tools and Memory.

In this guide, we look at how it works, various tools and frameworks to build context systems, and some real world examples.

Choosing The Right Context

Let’s go back to the assistant analogy. To answer that email, they need context about that specific sender and your relationship with them. So the context here isn’t about all your contacts, it’s about that specific one.

And that’s what context engineering is all about – building a system that can dynamically select the right context and feed it to our agents in the right format so that it has all the information it needs to successfully complete its task.

In fact, one may argue that missing context is one of the biggest reasons agentic systems fail. The context either doesn’t exist as data, or it isn’t being pulled in when needed.

Conversely, building a strong context management system is the key to a successful agent.

But hang on, why don’t we just give the agent all the information we have up front? Why do we need to select context?

Primarily because of token limitations. Just because you have a window of 1 million tokens (I’m looking at you, Gemini) doesn’t mean you should use it all. A typical enterprise knowledge base containing millions of documents would require tens of millions of tokens to include comprehensively, far exceeding any practical context window.

Additionally, LLMs suffer from “lost in the middle” effects where relevant information buried within extensive context receives inadequate attention. Models consistently perform better when critical information appears at the beginning or end of context windows.

Google’s “Chain of Agents” research found that systems using selective context outperformed full-context approaches by 10% while using significantly fewer tokens. Industry implementations consistently show 35-60% improvements in accuracy and response speed when using curated top-k document retrieval compared to comprehensive knowledge base access.

The anatomy of a context management system

Ok so we’ve established how context engineering is different than prompt engineering, and why we need a system instead of stuffing all the context into our window. Let’s look at the principles and components that make up this system.

Strategic Design Principles

Dynamic context construction is the foundation of effective systems. Instead of using static templates or fixed information sets, we build context actively for each interaction. This means analyzing the specific task, like in our AI assistant example, and using the tools and data at hand to select optimal context combinations in real-time.

Information relevance trumps information volume in every successful implementation. For example, recent communications with our email sender matters more than an email thread from 5 years ago. You’ll need to take into account factors like this when building your system.

Token efficiency drives every design decision in production systems, due to the aforementioned context window limitations. This means choosing information formats that convey maximum meaning in minimum space, eliminating redundancy, and prioritizing information density over comprehensiveness.

Technical Architecture Components

Context systems are built from six essential components that work together to provide comprehensive information environments.

System prompts establish the behavioral foundation, defining the AI’s role, capabilities, and constraints. These remain relatively stable but can be adapted based on task types or user contexts.

Memory systems provide continuity across interactions, maintaining both immediate conversation history and long-term learned information. Short-term memory tracks recent exchanges, while long-term memory preserves facts, preferences, and patterns that persist across sessions.

Retrieval systems dynamically incorporate external knowledge through RAG implementations that search, rank, and integrate relevant information from knowledge bases, documents, or real-time data sources.

Tool integrations expand capabilities beyond text processing, providing access to APIs, databases, calculation engines, and external services that enable the AI to perform actions and gather fresh information.

User input processing transforms raw queries into structured task specifications that guide context assembly and response generation.

Output formatting ensures responses meet specific requirements for structure, format, and processability by downstream systems.

Common pitfalls and how to avoid them

Context poisoning represents the most dangerous failure mode in context engineering systems. This occurs when errors or hallucinations enter the context and are repeatedly referenced, creating compounding mistakes over time. Prevention requires implementing context validation mechanisms, periodic cleaning processes, and explicit error detection systems. Recovery strategies include context quarantine systems and automated fact-checking against reliable sources.

Context distraction happens when excessive information causes models to lose focus on primary tasks. This manifests as off-topic responses or irrelevant information inclusion. Set up filtering and scoring systems that prioritize task-relevant information to avoid this.

Context confusion emerges when you have contradictory or poorly organized information. This happens often in systems with multiple sources of information and results in inconsistent outputs, logical contradictions, or inappropriate tone/style variations. Create clear information hierarchies and conflict resolution algorithms to identify and resolve contradictory information sources.

Tools and frameworks powering modern context engineering

LangChain and LangGraph provide comprehensive frameworks for agent orchestration and context management. LangChain offers context engineering primitives including memory management, tool integration, and retrieval systems.

LangGraph extends these capabilities with workflow orchestration, state management, and complex reasoning chains. Both frameworks support thread-scoped short-term memory, long-term memory persistence, and context compression utilities.

LlamaIndex specializes in data frameworks for knowledge-intensive applications. Its architecture supports advanced document parsing, multi-modal indexing, and context-aware chat engines. Memory implementations include VectorMemoryBlock for vector database storage, FactExtractionMemoryBlock for automatic fact extraction, and StaticMemoryBlock for persistent information.

Anthropic’s Model Context Protocol (MCP) has emerged as the industry standard for context integration. Released in November 2024, MCP provides an open-source protocol for connecting AI systems to data sources, tools, and external services. The protocol standardizes how AI systems access and utilize external information sources.

Specialized tools address specific context engineering challenges. RAGAS provides real-time context quality evaluation. LangSmith offers agent tracing and observability for debugging context flows. Promptfoo enables systematic testing of context and prompt combinations. These tools are essential for maintaining and optimizing context engineering systems in production environments.

Real World Example – AI Coding Agents

AI Coding agents are a great example of how context engineering can dramatically impact the behaviour of an agent. We already know that LLMs have reached a point where they are incredibly good at writing or debugging code.

The reason an agent like Claude Code or Amp Code feels like magic is its ability to pull in the right context. It has a three-layer context management system that goes far beyond simple prompt engineering. This architecture enables them to understand not just individual files, but entire project ecosystems with their dependencies, conventions, and architectural patterns.

When you initialize Claude Code in a new project, it first scans the entire codebase to understand project organization, identifying key files like package.json, requirements.txt, or build configuration files that reveal the project’s technology stack and dependencies.

It then creates a Claude.md file that serve as a persistent memory system and provides project-specific context to the AI. It contains project conventions, architectural decisions, coding standards, and any special considerations. For example, a Claude.md file might specify: “This project uses functional React components only, follows conventional commit format, and requires all database queries to use the existing ORM patterns.”

We then have a dynamic layer that manages real-time information gathering and context assembly for specific tasks. When you ask Claude Code to “fix the authentication bug,” it doesn’t just look at files with “auth” in the name, it analyzes the codebase to understand authentication flow, identifies related middleware, configuration files, and test files that might be relevant.

Once it pulls in this context, it creates a plan to fix the bug and presents it to the user. Now we have the final layer of context, which is the conversation. This includes what the user says to the agent, as well as the tools the agent uses and the data it gets back.

When it executes shell commands, reads file contents, or integrates with GitHub APIs, the results become part of the context that informs future decisions. This creates a feedback loop where action results improve subsequent reasoning.

Claude also has some nifty ways of managing when context gets too large like compacting a conversation (which includes all the results from tool calls).

Context Is All You Need

Like I said earlier, most failure modes in agents can be traced back to faulty context management. This is especially true in coding agents where the wrong context can lead to disastrous outcomes but the right context leads to vibe coding bliss.

As you design your systems, start simple, giving your agent limited context and enabling it to pick the most pertinent, and slowly scale up from there, adding more memory and tools to augment the context.