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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.
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.
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.
Technical Sharing
Connecting AI agents to internal systems via MCP has become standard practice, but NSA guidance, the Azure DevOps MCP flaw, and malicious skill campaigns show that integration has outpaced security. This article breaks down the two proven attack patterns and delivers a five-area pre-deployment security checklist enterprises can verify item by item.
Technical Sharing
Many enterprises place their AI CoE under the IT department, but this choice often limits how far AI adoption can be pushed. This article breaks down five reasons why an AI CoE should be set up independently.
Technical Sharing
Scattered enterprise knowledge is one of the biggest obstacles to scaling. This article provides three steps for building an effective knowledge management system: starting your inventory with high-value knowledge to identify priorities, designing a search architecture that lets knowledge actually be found, and building a mechanism for continuous knowledge updates — plus how AI semantic search and automatic knowledge extraction take knowledge management to a whole new level.
Technical Sharing
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