#AI agents

#AI agents

Agentic AI workflows sit at the intersection of automation and decision-making. Unlike a standard workflow, where data flows through pre-defined steps, an agentic workflow gives a language model discretion. The model can decide when to act, when to pause, and when to invoke tools like web search, databases, or internal APIs. That flexibility is powerful - but also costly, fragile, and easy to misuse.

Have you ever asked Alexa to remind you to send a WhatsApp message at a determined hour? And then you just wonder, 'Why can't Alexa just send the message herself? Or the incredible frustration when you use an app to plan a trip, only to have to jump to your calendar/booking website/tour/bank account instead of your AI assistant doing it all? Well, exactly this gap between AI automation and human action is what the agent-to-agent (A2A) protocol aims to address. With the introduction of AI Agents, the next step of evolution seemed to be communication. But when communication between machines and humans is already here, what's left?

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A2UI (Agent to UI) is a UI protocol from Google that lets AI agents generate user interfaces on demand. It introduces declarative mini-apps where UI components and actions are defined in a schema, and the agent can operate them automatically. Think Telegram-style mini-apps: small, self-contained interfaces that work without custom integration code. Instead of a long question-and-answer loop, agents can now send interactive, native interfaces directly to the client.

Ilan Zerbib, who spent five years as Shopify's director of engineering for payments, is building a solution that could eliminate these backend infrastructure headaches for non-technical creators. Last summer, Zerbib launched Sapiom, a startup developing the financial layer that allows AI agents to securely purchase and access software, APIs, data, and compute - essentially creating a payment system that lets AI automatically buy the services it needs.

Why is it that your existing employees initially outperform the new rockstar you've just hired? And why do you have a period of onboarding before a new hire gets up to speed? Institutional knowledge. The new rockstar knows how to do the job. That's why you hired them. But they need time to understand the company culture, processes, approaches, applications, their team, and customers and partners.

This is achieved via Model Context Protocol (MCP), an open protocol that lets AI agents work with external tools and structured resources. Xcode acts as an MCP endpoint that exposes a bunch of machine-invocable interfaces and gives AI tools like Codex or Claude Agent access to a wide range of IDE primitives like file graph, docs search, project settings, and so on.