Your skills are leaving your hands. Don't let a rent-a-brain keep them.

2026-06-17 · Source: Nate’s Substack · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The content introduces "Open Brain" and "Open Skills" as solutions to the problem of AI agents lacking user context and memory. It highlights the issue of personal data and work methods being trapped within individual AI applications, SaaS products, or model providers. The author previously discussed "Open Brain" to address the need for agents to access user context like current projects, past decisions, key contacts, and prior attempts, ensuring memory isn't siloed. This concept resonated due to the common frustration of repeatedly inputting personal context into new AI workflows. "Open Skills" extends this by aiming to keep a user's unique work methods independent of specific AI apps, preventing them from being "rented back" by dominant AI platforms.

Key takeaway

For AI Engineers developing agentic systems, you should prioritize architectural designs that enable agents to access and manage user context and memory independently of specific applications or model providers. This approach prevents vendor lock-in and eliminates the inefficient "re-explain my life" ritual, ensuring your agents can utilize a consistent, comprehensive understanding of user workflows and past decisions. Focus on open standards for context sharing.

Key insights

AI agents require user-controlled, unsiloed access to personal context and work methods to be truly useful and prevent vendor lock-in.

Principles

• AI agents need comprehensive user context.
• Memory and skills should not be app-trapped.
• User context prevents repetitive re-explanation.

In practice

• Design agents with external context access.
• Prioritize data portability for AI workflows.
• Avoid single-vendor AI memory solutions.

Topics

• AI Agents
• Context Management
• Data Portability
• Vendor Lock-in
• Personal AI
• Open Brain
• Open Skills

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, Software Engineer, AI Product Manager

Related on AIssential

• AI Coding Agents, Deconstructed — Effective AI agent systems require structured architectures that separate concerns to manage context and scale workflows.
• I Stopped Writing Longer Prompts and Started Writing “Skills.” — Encapsulating engineering procedures into contextually loaded "skills" dramatically improves AI agent reliability and discipline.
• Your Agent Forgot Everything Again. Here’s Why That’s a Design Problem. — LLM agents lack episodic memory, hindering their ability to learn from past interactions and maintain context.
• Your AI Agent Already Forgot Half of What You Told It — AI context loss is mitigated by externalizing information to persistent files.
• Stop Chatting With AI. Start Orchestrating It — AI orchestration centralizes agent management, but technical prompts can disrupt non-technical user flows.
• Context Engineering for AI Agents: Part 2 — Effective AI agent performance hinges on precise context management, minimizing irrelevant information and optimizing tool access.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Nate’s Substack.