Type: Web Article Original link: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus Publication date: 2025-09-24
Summary #
WHAT - This article discusses Context Engineering for AI Agents, sharing lessons learned during the development of Manus, an AI agent. It describes the challenges and solutions adopted to optimize the context of AI agents, improving efficiency and costs.
WHY - It is relevant for AI business because it offers concrete strategies to improve the performance of AI agents, reducing development times and operational costs. The techniques described can be applied to optimize AI agents in various sectors.
WHO - The main players are Manus, a company that develops AI agents, and the development team led by Yichao ‘Peak’ Ji. The article is aimed at developers and companies working on AI agents.
WHERE - It positions itself in the market of tools and techniques for the development of AI agents, offering best practices for context engineering.
WHEN - The article was published in July 2024, reflecting the lessons learned during the development of Manus. The techniques described are current and applicable in the context of today’s AI technologies.
BUSINESS IMPACT:
- Opportunities: Implementing context engineering techniques to reduce operational costs and improve the performance of AI agents.
- Risks: Not adopting these practices could lead to inefficiencies and high costs.
- Integration: The techniques can be integrated into the existing stack to optimize AI agents in various sectors.
TECHNICAL SUMMARY:
- Core technology stack: Uses context engineering techniques to optimize AI agents, with a focus on KV-cache hit rate. Languages mentioned: Rust, Go, React.
- Scalability: The techniques described are scalable and can be applied to various AI agents.
- Key technical differentiators: Use of KV-cache to reduce latency and costs, context engineering practices such as maintaining a stable prompt prefix and append-only context.
Use Cases #
- Private AI Stack: Integration into proprietary pipelines
- Client Solutions: Implementation for client projects
- Strategic Intelligence: Input for technological roadmap
- Competitive Analysis: Monitoring AI ecosystem
Resources #
Original Links #
- Context Engineering for AI Agents: Lessons from Building Manus - 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://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus
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