Type: Hacker News Discussion Original link: https://news.ycombinator.com/item?id=44427757 Publication date: 2025-06-30
Author: robotswantdata
Summary #
WHAT - Context Engineering is the practice of providing all the necessary context to enable a language model to solve a task. It includes instructions, conversation history, long-term memory, retrieved information, and available tools.
WHY - It is relevant because the quality of the context determines the success of AI agents. Most agent failures are not due to the model but to a lack of adequate context.
WHO - Key players include Tobi Lutke, who coined the term, and the AI community that is adopting this approach to improve the effectiveness of agents.
WHERE - It positions itself in the AI market as an advanced practice to improve the effectiveness of AI agents, integrating with existing techniques such as prompt engineering.
WHEN - It is an emerging concept, in a phase of increasing adoption, that is gaining traction with the growing use of AI agents.
BUSINESS IMPACT:
- Opportunities: Improve the effectiveness of AI agents through richer and more accurate context.
- Risks: Competitors who quickly adopt this practice may gain a competitive advantage.
- Integration: It can be integrated with the existing stack, improving the quality of AI agent responses.
TECHNICAL SUMMARY:
- Core technology stack: Includes instructions, user prompts, conversation history, long-term memory, retrieved information (RAG), available tools, and structured outputs.
- Scalability: Requires efficient management of memory and retrieved information to scale with increasing data.
- Technical differentiators: The quality of the context provided is the main success factor for AI agents.
HACKER NEWS DISCUSSION: The discussion on Hacker News highlighted the importance of the tools and architectures needed to implement Context Engineering. The community emphasized how context management is crucial for solving complex problems and improving the design of AI agents. The general sentiment is one of interest and recognition of the importance of context in improving the performance of AI agents. The main themes that emerged were the need for adequate tools, solving context-related problems, and the effective design of AI agents.
Use Cases #
- Private AI Stack: Integration into proprietary pipelines
- Client Solutions: Implementation for client projects
Third-Party Feedback #
Community feedback: The HackerNews community commented with a focus on tools, problems (20 comments).
Resources #
Original Links #
- The new skill in AI is not prompting, it’s context engineering - Original link
Article recommended and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-09-24 07:36 Original source: https://news.ycombinator.com/item?id=44427757
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