Field notes on agentic AI, the Model Context Protocol, observability, and building transparent, portable AI workflows.
Where the AI app builders shine, where they keep your software inside their walls, and what "Describe it. Ship it. Own it." actually means.
Read article →Where the three tools actually overlap, what self-hosting really costs for each, and an honest guide to choosing your agent stack.
Read article →Rules first, model second, humans last — hybrid cleaning pipelines, entity resolution without O(n²) costs, validation gates, and cost control.
Read article →Where triage agents actually work, the guardrails they need, and the three metrics that prove helpdesk automation is paying off.
Read article →Why agent loops make token math non-linear, how to compute cost per completed task, and the five cost levers ranked by impact.
Read article →Route each request by cost, latency, and task instead of paying frontier prices for everything — the strategies that work, and the hidden costs.
Read article →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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