Field notes on agentic AI, the Model Context Protocol, observability, and building transparent, portable AI workflows.
Transport, OAuth 2.1, scaling, and observability — what it actually takes to keep a remote MCP server alive in production.
Read article →The three MCP primitives, stdio vs. Streamable HTTP transport, a worked example, and what it takes to run a tool in production.
Read article →What observability actually requires for agents — traces, tool calls, per-step token cost, and replay — and how to instrument it.
Read article →How transparent, observable agents are reshaping the way teams build and deploy an AI workforce.
Read article →Bringing Groq's high-speed inference to OBTO to make agentic workflows faster and more cost-efficient.
Read article →A visual walkthrough of the OBTO Glass Box architecture — how the runtime, observability, and MCP layers fit together.
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