Mastering AI: 6 Crucial Context Engineering Lessons |

After building numerous no-code AI automations, it has become clear that context engineering is the most important factor in determining the quality and consistency of AI systems. This article will cover over five of the most important context engineering lessons learned from experience, breaking it all down and applying it to n8n AI agents.

An Introduction to Context Engineering

If it's a term you've never heard before, context engineering is the art of providing your AI agent with the right information it needs to complete tasks effectively. Unlike prompt engineering, which focuses on crafting the perfect single instruction, context engineering is about building systems that can dynamically provide relevant information to the agent.

In a nutshell, the agent should be able to receive a message that triggers it and understand the various tools and sources of data or information it can access. The core challenge is for the agent to decide which resources to use to find the information needed to answer a query or perform the right task.

The problem with most AI agents today that don't use extensive context engineering is that it's like having a conversation with someone who forgets everything right after you've said it. The solution is to ensure that after the agent reads its system prompt, it understands the different tools available to gather more context. This transforms it from a simple question-and-answer tool into an actual assistant that can remember things and take action intelligently.

This isn't a new concept. The foundation of AI systems and automations has always been data and context. The systems will only work as well as the data and context you feed them. Garbage in, garbage out. Especially when working on systems for a client, you must leverage their subject matter expertise to train the systems to behave the way an employee in their business would.

The 6 Core Components of Context Engineering

To understand the chronological flow of an AI agent, let's break down the main components:

User Input: This is the dynamic request we ask the agent to perform each time we trigger it.
System Prompt: This is where the agent reads its instructions to understand what tools it has, what it needs to do, and what the user is asking of it.
Memory: The agent can check past conversations to see if anything previously discussed can help it perform its job better.
Retrieved Knowledge: This goes hand-in-hand with tools. The agent can use its tools to access different knowledge bases, such as a vector database, an API call to search the web, or looking up something in a CRM.
Tools: These are the external systems the agent can interact with, like sending a message through Gmail.
Structured Output Parser: This is where we tell the agent how we need it to format the output information.

Not every agent or system needs all six of these components, but these are the different elements you can tweak and play around with.

A useful analogy highlights the difference between prompt engineering and context engineering: Prompt engineering is like studying for an exam the week before. Context engineering is like showing up to the exam with a cheat sheet you can look at every time you're faced with a problem you don't know. For the best results, you'll want both good studying and a good cheat sheet. But if you could only have one, the cheat sheet is arguably more valuable.

1. Memory Systems in AI Agents

There are three main categories of memory in n8n AI agents:

Working Memory: This is the agent's process of using its system prompt and chat model between actions to figure out what it just did and what it still needs to do on an execution-by-execution basis.
Short-Term Memory: This is essentially the conversation history, a brief context window of what has been said to whoever is currently interacting with the system.
Long-Term Memory: This is more persistent knowledge that can survive across sessions.

When all elements of proper memory are in place, an agent can remember user preferences, recall previous conversations, and maintain context across multiple sessions. For short-term memory, we can choose the context window length.

Consider this example conversation:

User: "Hey, my name's Nate." Agent: "Hey, how can I assist you?" User: "I have a dog named Workflow." Agent: "That's a great name. How old is Workflow? What kind of dog?" User: "He's a golden retriever. What should we do this weekend?" Agent: "Since Workflow is a golden retriever, a trip to a dog-friendly park or a lake for a swim would be a great activity..."

Because the agent has short-term memory, it can hold a coherent conversation. If our context window was set to two, it would only remember the most recent two interactions. A longer context window allows the agent to retain more conversation history, but it also processes more tokens, which can be more expensive.

Another key aspect is the session ID. This allows an agent to have unique conversations with person A, person B, and person C, and keep them all separate. You could use an email address as a session ID, so every time an agent receives an email, it looks up past conversations with that address.

For long-term memory, which is persistent across sessions, options include: * User Graph (e.g., with Zep): This stores user preferences and relationships between different pieces of information (e.g., what they like, where they live). * Google Doc: You can simply store memories in a document and instruct the agent to use that tool to look up information. * Vector Store: This allows for more efficient, chunk-based retrieval. * CRM: When a request comes in, the agent can look up the client in the CRM to find relevant information and tailor its response.

2. Using Tool Calling for RAG

Tool calling (or function calling) allows an agent to interact with external systems to retrieve data and perform actions beyond just generating text. It's like giving your agent hands and feet in the digital world.

Retrieval-Augmented Generation (RAG) is a technique where AI agents retrieve relevant external documents or data at query time and use it to respond more accurately. For example, if you asked an agent for the capital of California and it didn't know, it would use a tool to look it up (the retrieval part) to generate a more accurate answer.

While RAG is often associated with vector search, it can be much broader:

Vector Database RAG: A common flow involves putting a Google Doc into a Supabase vector store. The AI agent, equipped with a Supabase tool, can then query this store to answer specific questions about the document's content.
Web Research RAG: An agent can be given multiple research tools, like Perplexity and Tavily, to find up-to-date information on the web that wasn't part of its original training data.
Internal Systems RAG: Tools can connect to internal systems like HubSpot, Airtable, or Google Sheets to retrieve contact data, project information, or other business-specific details.

The real power emerges when an agent has access to various tools and can intelligently decide which one to use based on the user's query. This requires a good prompt engineering strategy.

3. The Nuances of Chunk-Based Retrieval

Chunk-based retrieval, typically involving a vector database, is a technique where large documents are broken down into manageable pieces that can be searched and retrieved more effectively. This is crucial because AI models have limited context windows; you can't drop a 65-page PDF into an agent and expect it to process it all at once.

However, the main issue with chunking is that you can lose the relationships and the context of the entire document. An agent asked to summarize a 65-page PDF that has been chunked would likely do a poor job.

Fortunately, we can make chunk-based retrieval more accurate:

Enhancing Retrieval with Metadata: Metadata—data about data—can enrich the context of chunks. For example, when chunking transcripts, you can add metadata like the original article title, URL, and a timestamp for each chunk. When the agent retrieves a chunk, it also gets this valuable context.
Improving Accuracy with Reranking: Instead of retrieving just the top three most relevant chunks, you could pull back ten. A reranker then assesses these ten chunks, keeps the top three most relevant ones, and feeds those to the agent, leading to more accurate results.

4. Optimizing Costs with Summarization Techniques

Summarization is the process of condensing large amounts of information into a concise, relevant summary that an AI model can process efficiently. This is vital not only for managing the context window but also for controlling costs, as more characters mean more tokens to process.

If we can summarize a large piece of text into its key points, the agent can still create a better answer while optimizing costs.

One effective method is to use a sub-workflow for RAG. Instead of the agent querying a tool and getting the raw, potentially large output, it queries a sub-workflow. This sub-workflow queries the tool, feeds the output into a summarization chain to make it more concise, and then feeds that summary back to the main agent. This approach keeps the essential information while being more cost-effective.

5. Adopting a Strategic Mindset

Developing the right mindset is crucial for effective context engineering. Here are several key principles to follow:

Begin with the End in Mind: If you have a high-level idea of the system you want to build, you'll know what the agent will be doing and the types of queries it will receive. Defining the queries and document types will help you decide if you need to fetch full files or if chunk-based semantic search is sufficient.
Design Your Data Pipeline: Think about all your data sources. Are they static or dynamic? How often do they update? You need to set up automations to keep everything relevant. Consider your refresh frequency and how to handle deletions to ensure data accuracy.
Ensure Data Accuracy: The goal of RAG is for the agent to pull back relevant, up-to-date, and accurate information. If your knowledge bases are outdated or inaccurate, the agent's answers will be too. A solid data pipeline with predictable inputs and standardization is the foundation.
Optimize Context Windows: Load only the most relevant information to control costs and prevent overload. Set up systems to ensure your agents only request the most relevant information based on the user's input. This leads to quicker, cheaper, and more consistent results.

6. Embrace AI Specialization

Whenever you use an AI agent in an automation, ask yourself: "How can I make this AI do one job really well?" While it's possible to create a super agent that handles many different jobs, it's often more effective to specialize.

Instead of giving one "Ultimate Assistant" all the tools for email, calendar, and contacts, give it fewer tools and have it delegate queries to the right specialized agent. If a process has four major steps—like research, writing a report, and creating a message—it will be more consistent to have a dedicated agent for each step.

Think of it like an assembly line. Each agent does one thing well and passes its output to the next step. This approach is more efficient and helps with prompting because you can write very specific instructions for each agent's single job. It also allows you to use different AI models for each step, leveraging the unique strengths of each model for a specific task.