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AI Model Routing: How to Choose by Task, Cost, and Risk
AI model routing matches each task with the right model, cost, and risk controls. Learn how to design rules, escalation, and useful metrics.
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Blog about AI, products, and building
Practical analysis of new AI models, tools, and product decisions from someone who actually ships them.
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How to Test AI Agents: Evals, Traces, and End-to-End Checks
AI agent testing goes beyond the final answer. Learn how to evaluate trajectories, tool calls, side effects, cost, and regressions.
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Skill, Subagent, or Multi-Agent System? How to Choose
Skills, subagents, and multi-agent systems solve different problems. Use this decision framework to choose the smallest architecture that works.
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The AI Agent Environment: Sandboxes, Logs, Permissions, and Verification
An AI agent is only as reliable as its environment. Learn how sandboxes, identity, permissions, logs, and outcome verification fit together.
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Context Engineering for AI Agents: Why More Context Is Not Better
Context engineering selects the instructions, memory, files, and tool output an AI agent needs. Learn how to reduce noise without losing evidence.
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What Is MCP and When Do You Actually Need Model Context Protocol?
MCP connects AI applications to data and tools. Learn how it works, how it differs from an API, and when the extra protocol is worth using.
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What Is an AI Agent and How Is It Different from a Chatbot?
An AI agent chooses actions, uses tools, and learns from environmental feedback. Here is how agents differ from chatbots and fixed workflows.
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What is Jev? TypeSafe AI's decision model explained
Jev returns typed decisions, not chatbot prose. See how it works, where it fits in AI agents, and why valid types do not guarantee sound judgment.