The RIGHT Method for Context Engineering (+3 Advanced Techniques)

Key Concepts

• Context Engineering: Providing AI with the right information at the right time, dynamically updated as the agent works.
• Long Horizon Agents: AI agents designed for complex, multi-step tasks.
• Asynchronous Context Synchronization: Using tools like Airbyte to sync data sources (e.g., Slack, Zendesk) to a vector database (e.g., Astra DB) for agent access.
• Self-Maintained Agentic Scratch Pad: An agent's ability to update and use its own memory or scratchpad during a process.
• Multi-Agent Context Compression: Ensuring reliable multi-agent systems by having agents share summaries and key decisions.
• MCP (Model Context Protocol) Servers: Servers that provide models with access to external tools and information.
• Workflow Files: Rules files that define how an agent should behave and what tools it should use for a specific task.

Context Engineering: The New Trend?

The video addresses the idea that context engineering is a new trend replacing prompting and vibe coding. The speaker argues that while the concept of providing the right information to models isn't new (e.g., PRDs, model context protocol), the crucial aspect of context engineering is providing the right information for the next step. This dynamic updating of context is what makes it game-changing.

Definition and Emergence of Context Engineering

The speaker defines context engineering as "the art and science of providing AI with just the right information at the right time." This means the context must be dynamically updated as the agent works, not just providing static files.

The emergence of context engineering is attributed to the increasing demand for AI agents that can perform complex tasks autonomously, such as booking entire trips. As task complexity grows, so does the amount of context needed. The context from each step impacts the context required for subsequent steps.

The Importance of Relevant Context

Even with larger and smarter models, providing only the most relevant context is crucial. Irrelevant information impacts accuracy, latency, and costs. The reliability difference between adding relevant vs. irrelevant context multiplies with each step in a long task. Just one mistake can ruin the entire process. Context engineering is essential for making long-horizon agents reliable.

The Five-Step Method for Context Engineering

The speaker presents a five-step method for context engineering:

1. Retrieve: Fetch available information relevant to the task from internal systems (e.g., Zendesk, Slack, Notion), the web, MCP servers, previous agent memory, or PRD files.
2. Integrate: Integrate the retrieved information into the agent's context window using chat history, the agent's state (e.g., OpenAI Agents SDK context variable), system prompts, or a vector database (RAG).
3. Generate: Generate a response using an LLM and tools. Context is essential for effective tool calls. Provide the most relevant context at the bottom of the conversation history. Use structured outputs and guardrails.
4. Highlight: Highlight the most relevant context from the response. Use observability (e.g., Adam Silverman's techniques) to understand what the agent has seen and what it's missing. Summarize and extract key moments and decisions to avoid overwhelming the agent in the next step. Fine-tune the agent or adjust system prompts and tool inputs.
5. Transfer: Transfer the most relevant context to the next agent or step. Update internal systems, send information to other agents, or add it to the agent's memory or scratchpad. This step updates the first step (Retrieve) to keep information relevant and up-to-date.

Three Advanced Context Engineering Techniques

The video presents three advanced techniques:

1. Asynchronous Context Synchronization

This technique uses Airbyte to sync data sources like Slack to a vector database like Astra DB.

• Process:
  • Connect Airbyte to Slack, specifying the threads look back window, start date, and channels to fetch.
  • Connect Airbyte to Astra DB, specifying the chunk size, fields to store as metadata, and text fields to embed.
  • Configure Astra DB with the API endpoint, keyspace, and OpenAI key for embeddings.
  • Select the streams (data to sync) and set the sync frequency.
  • Use MCP servers (e.g., Fast MCP) to query the vector database.
  • Integrate the MCP server into an AI IDE like Cursor.
• Example: Connecting Slack messages to Cursor allows the agent to access all project-related discussions and decisions, making it more effective for development tasks.

2. Self-Maintained Agentic Scratch Pad

This technique involves the agent updating its own scratchpad or memory. The video doesn't provide a specific example but mentions it as a known technique.

3. Multi-Agent Context Compression

This technique addresses the issue of information loss in multi-agent systems.

• Problem: Cognition argued that multi-agent systems are unreliable because agents often miss crucial details, leading to misaligned results.
• Solution: The speaker's framework allows for custom communication flows where agents send summaries and key decisions to each other.
• Implementation: Extend the parameters of the "send message" tool to include "key moments" and a "summary" of the conversation.
• Example: A demo agency uses a "cost analysis" tool. When agents share key decisions, the tool works correctly. Without sharing key decisions, the tool fails because the necessary information is missing.

Conclusion

Context engineering is crucial for building reliable AI agents, especially for complex, multi-step tasks. The key is to provide the right information at the right time, dynamically updating the context as the agent works. The five-step method and the three advanced techniques presented in the video offer practical ways to implement context engineering in real-world applications. The speaker emphasizes the importance of maintaining context relevance throughout the entire process, ensuring that agents have access to the information they need to perform their tasks effectively.