#AI agents

#AI agents

I'll run out of money.

We began GitHub Agentic Workflows as an investigation into a simple question: what does repository automation with strong guardrails look like in the era of AI coding agents? A natural place to start was GitHub Actions, the heart of scalable repository automation on GitHub. GitHub Agentic Workflows leverage LLMs' natural language understanding to let developers define automation goals in simple Markdown files describing the desired outcome.

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.