Field notes on agentic AI, the Model Context Protocol, observability, and building transparent, portable AI workflows.
A repo-scoped token can force-push to main. The safer way to connect GitHub to an agent: scoped verbs, a server-side key, and a receipt for every call.
Read article →The demo worked because you were the safety system. Production takes you out of the loop — here's the structural layer that has to replace you.
Read article →We took the human out of the deploy loop, and a dropped network reply published the same post twice — plus the two checks that keep an agent's writes to production safe.
Read article →No one's watching a 3 a.m. run. What keeps a scheduled agent safe: enforcement outside the model, approvals, idempotent writes, and a receipt by morning.
Read article →Most ways to wire a sheet to an agent are rented glue that breaks. Give it a few exact verbs you own — key server-side, a policy on every write.
Read article →Prompt rules are advisory — a model can ignore them. The guardrail that holds runs server-side, in front of the action, with a receipt for every attempt.
Read article →An "active app" the server remembers is lovely in a demo and a bug in production. Why we made every tool call carry its own scope.
Read article →Logging is what your code writes when it remembers. An audit trail is a record the agent can't avoid producing — and where it's made decides if you can trust it.
Read article →Triage and deflection are the easy half. The valuable half is an agent that safely takes the action, resolves the ticket, and logs every step.
Read article →The demo runs one happy user on clean data. Production adds tenancy, rate limits, drift, and cost — and the safe write path that closes the gap.
Read article →How OBTO hosts its own website on OBTO: pages as rows, line-level patches, sha256-gated deploys, and the one build step we kept on purpose.
Read article →What "connect Slack" really means, the two paths to get there, the scopes that matter, and the guardrails that let an agent post without keeping you up at night.
Read article →What "self-hosted" really means, a fair tour of the open-source and managed options, and the tradeoffs that decide which platform you own.
Read article →Every useful agent is a small pile of secrets. Where API keys leak — prompt, client, logs — and the one pattern that keeps them server-side.
Read article →How we built an agent runtime on a data-native platform: wrap-and-tee observation off the critical path, app-as-data, and what we've proven vs designed for.
Read article →The safe pattern for database access: expose scoped, typed tools instead of raw SQL, keep credentials server-side, and log every query.
Read article →Every AI run should leave a receipt. Why we gave each run an itemized, queryable ledger of models, tokens, cost, and tools, on by default.
Read article →Per-token pricing hides the bill until it's too late. Route easy calls to a cheap model, keep the strong one for hard calls, and see each run's cost as it happens.
Read article →The plain-English guide: what an MCP server is, the three things it exposes, and how local and remote servers differ.
Read article →A demo proves the idea. Production is the dozen layers underneath it — and why AI codegen makes the gap bigger, not smaller.
Read article →Most AI platforms start with a chat box. We started with the tools and the audit trail, and treated the interface as optional — here's why.
Read article →Wrap the ServiceNow Table API as narrow, least-privilege MCP tools an agent can call safely: credentials stay server-side, and every call lands on the Glass Receipt.
Read article →Models get deprecated, repriced, and knocked offline. Here's how we keep memory and the live workstream outside any single model or client — so the work stays yours.
Read article →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.
Read article →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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