How to Set Latency and Token Budgets for Production AI Agents
Set request, model, and tool budgets together so production AI agents remain responsive, observable, and bounded when context or tool loops grow.
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How Data Teams Can Give Developers Read-Only Looker Access Through MCP
A practical control model for giving developers AI-assisted access to governed Looker answers without turning an MCP integration into a broad production-data backdoor.
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From Implementation to Intent: Writing Engineering Contracts for AI
A practical framework for turning vague AI coding requests into reviewable engineering contracts that preserve architecture, quality, and ownership.
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How to Detect Silent Failures in n8n and LLM Automations Before
A production-minded monitoring design for n8n and LLM workflows: detect missing runs, stalled executions, partial completion, and degraded outputs before business teams report a problem.
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How to Keep AI-Assisted Pull Requests Reviewable
Use scope budgets, purposeful commits, evidence-rich pull requests, and required CI checks to keep AI-assisted changes understandable and safe to merge.
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When Self-Hosting a Small LLM Actually Makes Sense for Internal
Self-hosting a compact language model can be justified for narrow, repeatable internal workflows, but only when workload fit, operational ownership, and measured economics align.
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How Java Teams Should Choose Restate or Temporal for Long-Running AI
A practical decision guide for Java teams evaluating Restate and Temporal for reliable, long-running AI-assisted and business workflows.
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A Practical Sandbox Architecture for Internal AI Agents That Can
Internal agents become risky when model reasoning, tool permissions, credentials, and execution all share one trusted process. This reference design separates those responsibilities so teams can automate code and workflows...
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How to Measure AI Coding Agent Productivity Without Misleading Your
A practical scorecard for evaluating AI coding agents without relying on token counts, lines of code, or misleading pooled metrics.
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How to Build Optimistic React Forms That Recover Cleanly When Server
A practical React 19 pattern for fast forms that preserve user input, distinguish provisional UI from confirmed server data, and recover clearly when actions fail.
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