Everyone talks about "AI agents," but the real magic happens when you wire them into repeatable workflows that can run complex, multi-step tasks end to end. Here's what agentic workflows really are, how they work in today’s tools, and what you should know before you let them loose on real business processes.
"AI agents are finally getting a common language for talking to your apps, databases, and dev tools – and it is not another proprietary plugin format. Model Context Protocol (MCP) is emerging as the neutral standard that lets ChatGPT, Claude, Gemini and friends share the same tools instead of reinventing them for every platform."
"LLM agents used to wake up with amnesia every time you opened a new chat. Now, with persistent memory and long-term context, they can behave more like real collaborators than disposable tools. Here is how AI agent memory actually works today, why it is hard to get right, and what it means for how you build and use AI."
Everyone is racing to ship "autonomous agents", but almost nobody is measuring whether these things actually get real work done without babysitting. Here's a practical guide to benchmarking AI agents so you can separate slick demos from systems you can trust in production.
AI agents are starting to act on our behalf in the real world — trading, booking, coding, even touching production systems. When they "go rogue," it is usually not sci-fi evil but very real, very human design mistakes. This post unpacks the main failure modes so you know what to watch for before you hand the keys to your stack over to an autonomous agent.
Remember when AI was just autocorrect and voice assistants? Those days are gone.