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One of the most common questions executives ask right now sounds straightforward: is the agent reliable enough yet? It feels like the right place to start, but the framing quietly...
As enterprise AI agents move into production, organizations are confronting a growing reliability problem. Many teams are discovering that LLM performance alone does not determine...
AI sees extensive use across many industries, but its reliability still leaves something to be desired. In the year 2026, AI is everywhere. Schools, online journals, laboratories,...
AI agents are increasingly proficient at executing business tasks autonomously, but IT leaders are cautious about granting permissions to access enterprise systems. Part of the cha...
Over the past few years, artificial intelligence has evolved from a largely experimental research field into a fundamental part of modern software systems. Driving this change is a...
Discover how to test complex multi-agent AI architectures. This technical guide covers MAS topologies, the four-level testing model, common failure modes, automated evaluation fram...
The most dangerous assumption in enterprise AI right now is that smarter agents should automatically be given more autonomy. It sounds logical. If an AI agent can reason, plan, cal...
Intelligence stopped being the bottleneck. Almost nobody has rebuilt their engineering around that fact yet. For three years, the industry has obsessed over one question: can we bu...
One of the most interesting projects my colleagues have done with LLMs has been building a system with Bayer to allow pharmaceutical researchers to query decades of inf...
Same model, two agents, wildly different competence. The difference isn’t intelligence — it’s whether the task can be graded. One mental…Continue reading on Think in AI Agents »
Most "AI agents" shipping right now are demos wearing production paint. They answer questions fluently and break the moment they touch a workflow with money, state, or consequences...
The demo always works. That’s the trap. The real fight is reliability and a cost curve nobody budgets for.Continue reading on Medium »
Enterprise AI teams are giving agents more freedom at the same moment their confidence in automated testing is collapsing.Half of enterprises have deployed an AI agent or LLM featu...
I've lost count of how many enterprise AI rollouts I've watched go through the same arc. Month one: leadership demo, applause, a slide with a hockey-stick projection. Month six: a...
Your chaos experiments passed. Your RAG pipeline is lying to you anyway. I've watched this play out more times than I'd like to admit. A team runs a thorough chaos suite, including...
Like all AI interactions, if its important, you should really verify the answers you get - ask where /how the answer was obtained from. LLMs, because they are developed using infor...
Lately, I’ve seen a specific pattern emerge as organizations make AI claims. “We’re AI-first.” “We’re AI-native.” “We’re agentic.” The language is confident, forward-looking, and n...
Rethinking reliability for agents that run for days, not secondsContinue reading on Medium »
SPONSORED FEATURE: 696 experts find co-pilot welcome, autopilot not so much
The winning agents in the next cycle will not be the ones who adopt AI fastest, America Foy writes. They will be the ones who learn to verify AI output fastest.
AI agents are becoming increasingly popular among crypto users, with Circle CEO Jeremy Allaire predicting that billions of AI agents will be operating within five years.
AI severs confidence from correctness. Finding the areas that require scrutiny is much harder precisely because the output looks excellent everywhere.
AI can improve decisions or make them worse. The difference isn’t how much people use it, but whether they know when to follow its advice.
There is a category of production incident that engineering teams are not tracking yet — because it doesn't fit any existing postmortem template. The agent initiated an action. The...
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