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.
The bot, designated MJ Rathbun or crabby rathbun (its GitHub account name), apparently attempted to change Shambaugh's mind by publicly criticizing him in a now-removed blog post that the automated software appears to have generated and posted to its website. We say "apparently" because it's also possible that the human who created the agent wrote the post themselves, or prompted an AI tool to write the post, and made it look like it the bot constructed it on its own.
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 →
As enterprises shift from experimenting with AI agents to deploying them in production environments, Google is rolling out a structured skills program aimed at helping developers build, test, and operationalize AI agents using Google Cloud tools, specifically its Agent Development Kit (ADK). Named the Gemini Enterprise Agent Ready ( GEAR) program, the initiative packages hands-on labs, 35 free monthly recurring Google Skills credits, and badge-earning pathways into a track within the Google Developer Program.
Errors can quickly become a bottleneck if hallucinations multiply when agents interact, said Nicolas Darveau-Garneau, a former Google executive and author of the book " Be a Sequoia, Not a Bonsai." If a single agent has a 5% hallucination rate, then it's hard to daisy-chain multiple agents without a high risk of errors. That's because the risk increases exponentially, he told Business Insider.
What Is the Agent Internet? In early 2026, a new layer of the internet has emerged -- one built by and for AI agents. Over 95 platforms now exist where autonomous AI agents communicate, trade, create, play, govern, and conduct research. This is not a speculative whitepaper. It is happening right now. The Agent Internet is a decentralized network of platforms where AI agents -- not humans -- are the primary users.
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.
New research suggests an AI agent can't fully replace a human consultant - at least for now. Mercor, the AI training giant, tested how well leading AI models, acting as agents, performed real-world consulting, banking, and legal tasks. The models failed most of the time, but Mercor's CEO, Brendan Foody, told Business Insider that the results tell only part of the story.
That's the concept behind RentAHuman.ai, a website that garnered social media attention and drew 200,000 people to sign up over the past week. Describing itself as "the meatspace layer for AI," the website says that it allows human users to sign up to complete tasks for AI agents who want things done offline - since, obviously, AI can't yet visit a store or talk to someone face-to-face.
Kris Marszalek, CEO and co-founder of crypto and stock trading platform Crypto.com, has bought an expensive website. In this case it's AI.com, valued at one point at $100 million, which will serve as the online home for his new company of the same name. The website launch is being paired with a Super Bowl ad that will air this Sunday.
Lofty AOS coordinates a suite of AI agents that operate simultaneously. One assistant prioritizes tasks tied to lead management. A sales-focused agent engages and qualifies leads, generates call scripts and analyzes sales calls while a social media agent creates and manages content strategies, including scheduling and posting. The system also includes a homeowner-focused agent that adds to contact databases and automates valuation-based outreach aimed at potential sellers.
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.
On Thursday, Anthropic and OpenAI shipped products built around the same idea: instead of chatting with a single AI assistant, users should be managing teams of AI agents that divide up work and run in parallel. The simultaneous releases are part of a gradual shift across the industry, from AI as a conversation partner to AI as a delegated workforce, and they arrive during a week when that very concept reportedly helped wipe $285 billion off software stocks.
Who were your investors and how much did you raise? We raised $12M in a Series A round. The round was led by Standard Capital with participation from a16z, CRV, and Y Combinator. We were also lucky to have an incredible group of angel investors join, including SV Angel, Ritual Capital, and several world-class CFOs and operators from companies like OpenAI, Vercel, Cursor, Carta, 1Password, and Brex.
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.
