Context Engineering: Building Smarter AI Agents - Part 3/3

Best Practices for Context Engineering with LangGraph

In Part 1 and Part 2 of this series, we introduced Context Engineering and explored how to use LangGraph to build context-aware AI applications, however, building robust and intelligent LLM applications with LangGraph requires more than just understanding the syntax; it demands a strategic approach to context engineering. Here are some best practices to guide your development:

Define Clear Goals and Constraints

Before you even start coding, clearly articulate what you want your LLM application to achieve. What are its primary functions? What are its limitations? Understanding the scope and purpose will help you identify what context is truly necessary and what is superfluous. For instance, a customer service bot needs different context than a creative writing assistant.

Curate and Structure Context

The quality of your LLM’s output is directly proportional to the quality and relevance of the context it receives.

• Relevance is Key: Only provide information that is directly pertinent to the current task. Overloading the context window with irrelevant data can lead to confusion, increased token usage, and diminished performance.
• Precision Matters: Ensure the information is accurate and unambiguous. Vague or contradictory context will lead to vague or contradictory responses.
• Structure for Clarity: Present context in a clear, consistent, and structured format. Use JSON, XML, or well-defined Markdown to organise information. This makes it easier for the LLM to parse and extract the necessary details.
• Prioritise Information: If context window limits are a concern, prioritise the most critical information. Techniques like summarisation or hierarchical retrieval can help manage large volumes of data.

Implement Robust Memory Mechanisms

Effective memory management is fundamental to maintaining coherent and personalised interactions.

• Short-term Memory (Conversation History): For ongoing dialogues, implement strategies to manage conversation history. This might involve:
  • Summarisation: Periodically summarising past turns to condense the history while retaining key information.
  • Windowing: Keeping only the most recent N turns or a fixed number of tokens.
  • Hybrid Approaches: Combining summarisation with a sliding window.
• Long-term Memory (Knowledge Bases): For information that needs to persist across sessions or is too large for the context window, integrate with external knowledge bases. This typically involves:
  • Vector Databases: Storing embeddings of documents and retrieving relevant chunks based on semantic similarity (as seen in the RAG example).
  • Traditional Databases: Storing structured data like user profiles, preferences, or past transactions.
  • Knowledge Graphs: Representing complex relationships between entities for more sophisticated retrieval.

Strategic Tool Integration

Tools extend the capabilities of your LLM, allowing it to interact with the real world and access up-to-date information.

• Define Tool Capabilities Clearly: Ensure your tools have clear, concise descriptions that accurately convey their function to the LLM. The LLM relies on these descriptions to decide when and how to use a tool.
• Handle Tool Outputs: Design your graph to effectively process and integrate tool outputs back into the context. This might involve parsing JSON responses, summarising long text outputs, or handling errors gracefully.
• Conditional Tool Use: Leverage LangGraph’s conditional edges to enable the LLM to dynamically decide whether a tool call is necessary based on the current query and context. This prevents unnecessary tool invocations and improves efficiency.

Iterative Development and Testing

Context engineering is rarely a one-shot process. It requires continuous refinement.

• Start Simple: Begin with a basic graph and gradually add complexity. Test each component and connection thoroughly.
• Test Edge Cases: Don’t just test happy paths. Actively try to break your system by providing ambiguous queries, unexpected inputs, or requests that require complex reasoning or tool use.
• Monitor Performance: Track metrics like response time, accuracy, and token usage. Identify bottlenecks or areas where context management can be improved.

Leverage Observability Tools (e.g., LangSmith)

Tools like LangSmith (from the creators of LangChain and LangGraph) are invaluable for debugging and understanding your LLM applications.

• Trace Agent Calls: LangSmith allows you to visualise the execution flow of your LangGraph application, showing you exactly what context was passed to each node, what the LLM’s thought process was, and what tool calls were made. This is critical for identifying where context might be failing or where the LLM is misinterpreting information.

• Evaluate Context Effectiveness: Use these traces to evaluate whether the context you are providing is indeed leading to the desired outcomes. Adjust your context engineering strategies based on these insights.

Conclusion: The Future is Context-Aware AI

It’s clear that the future of AI development, particularly with LLMs, lies beyond simple prompting. It resides in the meticulous design and management of the information environment in which these powerful models operate.

Context engineering is not merely an optimisation; it is a fundamental shift in how we build intelligent systems. By actively curating, structuring, and dynamically managing the context, we empower LLMs to move from being reactive text generators to proactive, reasoning agents capable of tackling complex, real-world problems with unprecedented accuracy and reliability.

LangGraph stands out as an indispensable framework in this new paradigm. Its graph-based architecture provides the necessary control and flexibility to orchestrate intricate workflows, manage state across multiple turns, integrate external tools seamlessly, and facilitate sophisticated multi-agent collaborations. It transforms the abstract challenge of context management into a concrete, programmable solution, enabling developers to build truly robust and context-aware AI applications.

As you embark on your journey to build more intelligent and capable LLM applications, remember that the key to unlocking their full potential lies in mastering context. Embrace context engineering, leverage the power of frameworks like LangGraph, and contribute to the exciting future of AI.