#AI agents

#AI agents

An AI agent is simply a model that receives input, follows defined goals and rules, makes step-by-step decisions, and uses tools to take actions. Instead of viewing AI agents as autonomous digital workers, break it down. First principles thinking says this definition captures the essence of how AI agents function and operate within business workflows.

The new battleground in banking is intelligent operations and scalable execution. In 2026, banking is about moving money smarter, faster, and with fewer humans in the middle. Across corporate finance and global retail operations, banks are experimenting with technology and operational design in ways that challenge long-held assumptions about scale, speed, and control. Three recent developments exemplify what's happening in money movement: Goldman Sachs deploying AI agents, Truist automating corporate receivables, and Nubank expanding abroad with a lean digital model.

Just consider a typical day in the life of a modern human: you glance at your phone while waiting for coffee to brew, skim headlines while half-listening to a podcast, mentally rehearse a client pitch while walking your child to school, reply "noted" on Slack during a meeting while updating a slide deck, check your bank balance while standing in line,

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?

Take2 - $14M Series A Take2, an AI agent network that automates end-to-end healthcare recruiting tasks, has raised $14M in Series A funding led by Human Capital. Founded by Yaniv Shimoni and Kaushik Narasimhan in 2023, Take2 has now raised a total of $14M in reported equity funding. AlleyWatch is NYC's leading source of tech and startup news, reaching the city's most active founders, investors, and tech leaders. Advertise today →

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.