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Technical Sharing
Before handing over the code, what an owner most wants to know is "what exactly are you looking at." This article spells out the four areas of a system health check and the three things actually reviewed in each, plus the process: a 60-minute meeting, no production credentials needed, a one-page report, and one of three conclusions.
Technical Sharing
A system that's been called both "don't touch it" and "beyond saving" shouldn't have its fate decided by gut feeling or by whoever wants the rebuild contract. This article gives four checkpoints: is it still readable, can the data be exported, can the architecture hold for two more years, and is fixing versus rebuilding really the same math — each with signals pointing toward saving it or toward rebuilding.
Technical Sharing
After an engineer leaves, a system keeps running and most owners choose not to touch it — until a credential expires, a hosting payment fails, or a vulnerability gets exploited, and only then do they find the password isn't even in the company's hands. This article lists 12 items to recover first, three ways to proceed when they're out of reach, and the first thing to do once you get them back.
Technical Sharing
Most brands install a separate AI bot on each channel, so voice, chat and CRM talk past one another and the same customer is split into disconnected fragments. This article breaks down CXM's three-layer cross-channel integration—unified customer view, system integration and cross-channel orchestration—backed by real data, so every interaction builds on the same customer record.
Technical Sharing
Why does an AI grow costlier and more scattered the longer a conversation runs? Drawing on the latest research from Chroma, Anthropic, and others, this article breaks down four context-engineering strategies — sliding window, summarization (compaction), chunk offloading, and pinning key information — and how to combine them.
Technical Sharing
Many companies treat AI delegation as an all-or-nothing switch—either every action is human-reviewed or the whole thing runs on its own—yet Gartner expects over 40% of agentic AI projects to be canceled by 2027 for weak risk controls. Drawing on real human-factors and AI-agent frameworks, this article lays out a five-tier delegation ladder from full human review to full autonomy, covering quantified promotion thresholds and a circuit-breaker fallback when quality slips.
Technical Sharing
When user instructions are vague, an AI system that simply guesses produces a flood of wrong output. This article walks through intent classification, slot filling, the clarification loop, confidence thresholds, and prompt patterns—an engineering approach that makes the system clarify when uncertain instead of guessing.
Technical Sharing
The hard part of making an AI agent act on its own is not the model but the machinery behind it: what wakes it, how events flow, and how it is watched and corrected. This article takes a purely engineering view — using real frameworks and data from Kafka, Debezium, OpenTelemetry, and Stripe — to break down triggers, the event bus, asynchrony, idempotency and retries, and the observability pipeline, showing how to turn autonomous AI into a system that is triggerable, observable, and recoverable.
Technical Sharing
Many companies want to hand repetitive web tasks to AI but cannot tell when to use browser automation versus an API. Using public benchmark data, this article clarifies the capability boundary—what browser automation can and cannot do, and a decision sequence for when to reach for it.
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