/blog · playbook notes

Notes from the workshop floor.

Slow, careful posts on what we've learned shipping AI-native apps.

From Maia Chips to Goal-Driven Agents: The New Architecture of Production AI

AI progress is shifting away from demonstrations and toward production constraints. The important questions are increasingly operational: How much does inference cost? Can agents work safely across business systems? Who verifies machine-generated code? Can enterprise teams control execution, privacy, and spending?

Oct 12, 2026Read

5 min

Building Trustworthy AI Systems: Agent Verification, Secure Models, and Efficient Infrastructure

AI is moving from isolated demonstrations into production workflows. The important question is no longer whether a model can generate code, answer questions, or operate tools. It is whether the surrounding system can make those capabilities reliable, secure, affordable, and maintainable.

Oct 9, 2026Read

5 min

Building Reliable Agentic AI: Infrastructure, Interfaces, Security, and Evaluation

AI is moving from chat interfaces into systems that search data, call tools, modify records, and complete multi-step workflows. This transition is creating demand across cloud infrastructure, developer tools, enterprise software, and AI security—but it is also exposing weaknesses in how these systems are evaluated and controlled.

Oct 8, 2026Read

5 min

From Single Chatbots to Governed AI Systems

AI progress is increasingly defined by system design rather than model quality alone. The emerging stack combines multiple models, autonomous developer tools, security testing, enterprise databases, and creative interfaces. Together, these developments point to a shift from asking one chatbot for an answer to managing an AI system that selects tools, changes software, and operates across production environments.

Oct 7, 2026Read

5 min

From Mistral Large 4 to Production Agents: The Infrastructure Behind Enterprise AI

AI is moving from impressive demonstrations toward operational software. The most consequential developments are no longer only about model benchmarks. They concern open-weight models, coordinated agents, realistic testing, permission systems, observability, and the cost of running complex workflows.

Oct 6, 2026Read

5 min

From Coding Agents to Governed AI Systems: Testing, Tooling, and Enterprise Infrastructure

AI development is moving beyond chat interfaces and autocomplete. Coding agents can now inspect repositories, edit production code, run commands, connect to business systems, and coordinate specialized models. That capability creates a new engineering challenge: making agentic systems structured, testable, secure, and operationally accountable.

Oct 5, 2026Read

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