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Notes on AI, agents, web technologies, and building with LLMs

Technical articles on AI integration, agents, agent harnesses, MCP, web technologies, and emerging tools. Working notes from a Google Developer Expert with 25+ years of building and teaching.

241 articles · Page 2 of 25

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  1. MCP Apps

    For its first eighteen months MCP let a model call a tool and read the result back as text. That's a low ceiling the moment a user wants to sort, filter or drill into something. MCP Apps raise it: the server ships an actual interface into the chat. Here's how MCP got here, what the new proposal says, and a small app you can run in Claude Desktop, built on the official SDK and checked against the spec.

    AIMCPMCP AppsAgents

  2. Joining the Agentic AI Foundation as an ambassador

    I've been accepted into the Agentic AI Foundation's ambassador programme. Here's what the foundation is, why it matters that this work is happening in the open, and what I've actually signed up to do.

    AIAgentic AIMCPOpen SourceCommunity

  3. Sparse vs Dense Retrieval: Why Your Search Doesn't Understand Synonyms

    If your retriever misses a relevant document because the user phrased their question differently than the document's author wrote the content, the LLM generates an answer with incomplete context. It doesn't hallucinate because it's confused, it hallucinates because it never saw the right information.

    AIRAG

  4. AgentTool vs sub-agent delegation

    Two ways to give an agent help: a tool it calls and gets an answer back from, or a sub-agent it hands the whole turn to. Same data-agent, opposite control flow. When to reach for each, shown with a real NL2SQL-to-chart pipeline in Google ADK.

    AIAgentic AIGeminiADK

  5. What is Loop Engineering

    Everyone is quoting "design loops that prompt your agents" and few can say what a loop actually is. Here's the plain version, plus the part the hype skips: the loop is the easy bit. What's hard is that it makes your understanding optional, and nothing in it tells you when you've stopped understanding what ships.

    AIAgentic AIAgentsLoop EngineeringAutomation

  6. Kitsune: a local-first agent runtime

    I built a managed-agent runtime that runs entirely on one laptop. In this post I show you how you can set it up.

    AIAgentsGeminiTypeScriptDocker

  7. Build Your Own Agent Harness

    What an agent harness is, how you build one, and why it is where the work now lives. I start from a bare loop, break it on real tasks, and walk the five places every harness ends up reaching for.

    AIAgentic AIAgentsHarness Engineering

  8. Agent Experience (AX) and the Agent Experience Interface (AXI): a working demo

    A companion to my AX and AXI piece. I built one fictional CLI twice, once human-only and once agent-ready, pointed a small Google ADK agent at each, and watched what happened. With the interactive demo and the real numbers.

    AIAgent ExperienceAXAXIAgentsCLI

  9. Agent memory, end to end

    How AI agents remember across conversations: the layers of agent memory borrowed from cognitive science, and the techniques that make them work, from character-capped fact stores to compaction, hybrid retrieval, query expansion, and memory consolidation.

    AI AgentsMemoryArchitecture

  10. Agent Experience (AX) and the Agent Experience Interface (AXI): a working guide

    What Agent Experience means, where AXI fits inside it, and what companies that take both seriously are actually changing about their products. With the official sources and the benchmark numbers.

    AIAgent ExperienceAXAXIAgentsProduct Strategy