The current message from engineering leadership at most companies I talk to is some version of: "find the deterministic automation in your stack and make it agentic." A recent r/devops thread captured the frustration: an SRE asked how to push back on a director who wanted every

Part 2 of 2 on agentic system adoption. Part 1 covered internal adoption: getting your own team to actually use the agent you built. In February 2024, Klarna announced that its AI assistant had handled 2.3 million customer conversations in its first month — two-thirds of all customer service chats,

Part 1 of 2 on agentic system adoption. A 25% bump in individual AI usage correlates with a 1.5% drop in delivery throughput and a 7.2% drop in delivery stability. That's not a hot take — that's the 2024 DORA report, surveying thousands of engineers

Since Anthropic released Claude Opus 4.7 on April 16, 2026, a consistent complaint has rippled through developer communities: it feels slower. Threads tell roughly the same story — the prompts that worked perfectly on Opus 4.6 now take noticeably longer to return, sometimes pause for what feels like forever

Anthropic's official plugin directory has 55+ curated Claude Code plugins since launch. The community tracks another 72. By end of 2026, most large engineering orgs will have a private marketplace too — and most of them will ship the architecture wrong. The mistake: treating the marketplace as a distribution

Six frontier coding models now score within 0.8 points of each other on SWE-bench Verified. The same model wrapped in different agent frameworks swings almost ten points on SWE-bench Pro. Picking an agent platform based on which model it runs misses where the real performance differences come from. The

Intercom's Fin AI agent went from $1M to $100M+ ARR on one pricing move: $0.99 per resolved ticket. Not per seat, not per month. Per outcome. Fin now handles 80%+ of Intercom's customer support volume and closes about a million conversations a week, and Intercom

Every week someone posts about AI hallucination like it's a mystery. It's not. A 2025 Frontiers in AI study measured it: vague, multi-objective prompts hallucinate 38.3% of the time. Structured, single-focus prompts? 18.1%. That's a 20-point accuracy gap from how you write

Most AI coding tools ship as monoliths. One big system prompt, one set of capabilities, one-size-fits-all. That works fine for general software engineering. It falls apart the moment you need domain-specific workflows that vary by role, by team, and by engagement. I build presales systems at Presidio — client research, SOW