Context Engineering for AI Agents: Lessons from Building Manus

Q: "How can AI agents benefit my business?"

"AI agents can automate complex multi-step tasks like data analysis, document processing, and customer interactions. For European SMEs, deploying agents on private infrastructure with tools like ORCA ensures that sensitive business data never leaves your perimeter while still leveraging cutting-edge AI capabilities."

Q: "Are AI agents safe to use with company data?"

"It depends on the deployment. Cloud-based agents send your data to external servers, creating GDPR risks. Private AI agents running on your own infrastructure — like those built on HTX's PRISMA stack — keep all data within your control. This is the safest approach for businesses handling sensitive information."

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#### Source

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

The HTX Take
#

This topic is at the heart of what we build at HTX. The technology discussed here — whether it’s about AI agents, language models, or document processing — represents exactly the kind of capability that European businesses need, but deployed on their own terms.

The challenge isn’t whether this technology works. It does. The challenge is deploying it without sending your company data to US servers, without violating GDPR, and without creating vendor dependencies you can’t escape.

That’s why we built ORCA — a private enterprise chatbot that brings these capabilities to your infrastructure. Same power as ChatGPT, but your data never leaves your perimeter. No per-user pricing, no data leakage, no compliance headaches.

Want to see how ready your company is for AI? Take our free AI Readiness Assessment — 5 minutes, personalized report, actionable roadmap.

FAQ

How can AI agents benefit my business?

AI agents can automate complex multi-step tasks like data analysis, document processing, and customer interactions. For European SMEs, deploying agents on private infrastructure with tools like ORCA ensures that sensitive business data never leaves your perimeter while still leveraging cutting-edge AI capabilities.

Are AI agents safe to use with company data?

It depends on the deployment. Cloud-based agents send your data to external servers, creating GDPR risks. Private AI agents running on your own infrastructure — like those built on HTX's PRISMA stack — keep all data within your control. This is the safest approach for businesses handling sensitive information.

Articoli Interessanti - This article is part of a series.

Part : GitHub - arman-bd/guppylm: A ~9M parameter LLM that talks like a small fish.

Part : Embarrassingly Simple Self-Distillation Improves Code Generation

Part : Show HN: 1-Bit Bonsai, the First Commercially Viable 1-Bit Large Language Models

Part : PrismML — Concentrating Intelligence

Part : GitHub - microsoft/VibeVoice: Open-Source Voice AI

Part : GitHub - karpathy/autoresearch: AI agents automatically conducting research on single-GPU nanochat training

Part : GitHub - Pinperepette/snakebite: Detect malicious PyPI packages using heuristic analysis and LLM-powered filtering to uncover credentials

Part : GitHub - 666ghj/MiroFish: A Simple and Universal Swarm Intelligence Engine, Predicting Anything. A concise and universal swarm intelligence engine, predicting everything.

Part : Introducing Mistral Small 4 | Mistral AI

Part : GitHub - andrewyng/context-hub

Part : Coding My Handwriting — Amy Goodchild