Summary

The article discusses the evolution of context engineering as a critical discipline in AI engineering, highlighting five key patterns for managing context effectively. It emphasizes the importance of context in optimizing the performance of large language models (LLMs) and addresses challenges such as context bloat and the need for efficient retrieval strategies. The author shares insights from previous works and workshops, providing a comprehensive overview of how context engineering has matured over the past year. The article serves as a guide for AI engineers looking to enhance their systems through improved context management.

💭 Your Thoughts

my notes: Finding the smallest possible set of high-signal tokens that maximise the likelihood of desired outcomes: system instructions, tool definitions, MCP resources, retrieved documents, conversation history, and accumulated action history. - Progressive disclosure loads information in tiers based on relevance. Discovery first (just names and descriptions), activation when relevant (full instructions), execution only during the task (scripts and reference materials). - Context compression shrinks accumulated history while preserving the information the model needs. Keep only top N turns of interaction, - Context Routing: uses the model itself to classify the query and select the appropriate context source, uses a lead agent to triage queries to specialised sub-agents - The agent decides its own search strategy, can reformulate queries when results are insufficient, and iterates until confident. trains models to decide when to retrieve and to critique their own outputs. - MCP server problem: Description quality, Too vague and the model picks the wrong tool.Tool overlap, No versioning for tool contract

Data Problems Addressed