Insights from the Glass Box

Field notes on agentic AI, the Model Context Protocol, observability, and building transparent, portable AI workflows.

Engineering Articles

• MCP Server as a Service: Your Data, Behind Your Firewall
  Every AI client now speaks MCP. The question is whose server exposes your business data — and whether it runs behind your firewall, governed and battle-tested.

• OBTO vs Lovable & Base44: Vibe Coding You Can Actually Own
  Where the AI app builders shine, where they keep your software inside their walls, and what "Describe it. Ship it. Own it." actually means.

• OBTO vs LangSmith vs Langfuse: Choosing a Self-Hosted AI Stack
  Where the three tools actually overlap, what self-hosting really costs for each, and an honest guide to choosing your agent stack.

• LLM Data Cleaning: Patterns for Production ETL Pipelines
  Rules first, model second, humans last — hybrid cleaning pipelines, entity resolution without O(n²) costs, validation gates, and cost control.

• AI Ticket Triage: How Agents Automate the Helpdesk
  Where triage agents actually work, the guardrails they need, and the three metrics that prove helpdesk automation is paying off.

• AI Agent Cost Tracking: Know What Every Run Costs
  Why agent loops make token math non-linear, how to compute cost per completed task, and the five cost levers ranked by impact.

• Multi-Model Orchestration: Routing LLMs by Cost and Task
  Route each request by cost, latency, and task instead of paying frontier prices for everything — the strategies that work, and the hidden costs.

• How to Host a Remote MCP Server in 2026: A Practical Guide
  Transport, OAuth 2.1, scaling, and observability — what it actually takes to keep a remote MCP server alive in production.

• How to Build an MCP Tool: A Practical Guide
  The three MCP primitives, stdio vs. Streamable HTTP transport, a worked example, and what it takes to run a tool in production.

• AI Agent Observability: A Practical Guide for Teams
  What observability actually requires for agents — traces, tool calls, per-step token cost, and replay — and how to instrument it.

Vision Articles

• How OBTO is Defining the AI Workforce of the Future
  How transparent, observable agents are reshaping the way teams build and deploy an AI workforce.

• Support for Groq: Fast Inferencing for Better AI Workflows
  Bringing Groq's high-speed inference to OBTO to make agentic workflows faster and more cost-efficient.

Architecture

Design & Architecture

A visual walkthrough of the OBTO Glass Box architecture — how the runtime, observability, and MCP layers fit together.