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Technical Sharing
After launch, AI systems inevitably hit hallucinations, API timeouts, and runaway costs, yet most teams have no plan for when an incident strikes. This article lays out an SRE-style AI incident response runbook covering SEV grading, response steps and roles, and blameless postmortems — grounded in real cases — that keep incidents from recurring.
Technical Sharing
The MCP 2026-07-28 specification makes the protocol core stateless and deprecates Roots, Sampling, Logging and the HTTP+SSE transport — the twelve-month window has already started. This article explains the real impact on existing enterprise systems, the infrastructure cost it saves, and a thirty-day inventory and migration checklist.
AI Research
Most enterprise AI pilots fail not on technology but on picking the wrong first use case. This six-dimension weighted scoring matrix turns gut feel into comparable scores, so you can select the lowest-risk, highest-success AI launch.
AI Research
Once an AI employee goes live, output quality quietly drifts and degrades with no one noticing—studies show a model's accuracy can halve within three months. This article focuses on post-launch tracking: which metrics to instrument, where the data comes from, how to tier alert thresholds, and a weekly/monthly/quarterly review cadence that makes AI performance visible and manageable.
Technical Sharing
Most Multi-Agent projects fail because they never chose the right collaboration topology, not because the agents were too weak. This guide maps four topologies—orchestrator-worker, hierarchical, peer, and pipeline—to the real usage and benchmark data of LangGraph, Anthropic, CrewAI, OpenAI, and MetaGPT so you can choose.
AI Research
Many companies run a probation for their AI employees but end up deciding on gut feel whether to confirm the hire—while MIT research shows 95% of generative-AI projects deliver no measurable results. This article gives four quantitative acceptance gates, the go/no-go decision logic, and a pre-confirmation checklist so you decide with data, not impressions.
AI Research
For AI systems, "correct" is not binary—third-party evaluation roundups put model hallucination rates between 15% and 52%, and a traditional availability SLA simply cannot govern that. This article offers an AI SLA framework covering both availability and quality, complete with production-grade thresholds and evaluation tooling, so selection and acceptance have an objective basis.
Technical Sharing
An AI system that shines in the demo but falls apart in production is a gap almost every adoption team has faced. Drawing on data from Gartner, RAND and MIT and a real legal precedent, this article dissects the technical debt a POC accumulates from an engineering angle and lays out a strangler-pattern layered-replacement strategy plus the engineering foundation you need before launch.
AI Research
Many enterprises compare AI platforms by monthly fee alone, then quietly pay several times more in integration, operations, and exit-migration costs. Using verifiable 2026 pricing, this article puts self-hosted OpenClaw and commercial platforms into one three-year TCO spreadsheet and finds your break-even point.
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