#ai-agents

#ai-agents

Mbodi wants to make training robots easier and quicker with the help of AI agents. The company will be showcasing this tech as one of the Top 20 Startup Battlefield finalists at TechCrunch Disrupt 2025. New York-based Mbodi built a cloud-to-edge system, a hybrid computing system using both cloud and local compute, that is designed to integrate into existing robotic tech stacks. The software relies on a multitude of AI agents that communicate with each other to gather the needed information to help a robot learn a task faster.

The third shift is in the way that AI is reshaping developer choice, not just code. In the past, developer choice referred to choosing an IDE, language, or framework. In the present timeframe, that has changed. GitHub now sees a correlation between the rapid adoption of AI tools and evolving language preferences. This and other shifts suggest AI now influences not only how fast code is written, but which languages and tools developers use.

When AI is a bubble, and talking about AI being a bubble is a bubble ... what do you do? Right, you start talking about AI agents. And AI... agentic... what does it matter? Once you put out a new message, you quickly find a small group of people most likely to respond. You harvest that group fast, performance drops, you change the message, find a new cohort, repeat.

Imagine an always-on learning partner that knows what you don't, nudges you at just the right moment, and turns busy work into bite-sized growth. That's the promise of learning co-pilots-intelligent AI agents embedded into daily workflows to guide, teach, and coach employees at scale. Not a replacement for instructors or mentors, these co-pilots augment human capability: they make learning contextual, timely, and measurable.

A Spooky Good Discount for the Final 100 ODSC AI West Passes Join the world's leading AI training conference in San Francisco & online, Oct 2830, and learn from experts at OpenAI, LangChain, DeepMind, Stanford, and more. Get the deal here and join us next week! Context Engineering for Agentic Applications In this article, you'll see how context transforms ordinary models into persistent, autonomous collaborators and why mastering it will be the defining skill for AI engineers in 2025 and beyond.

From crafting replies that sound just like you to handling high volumes of DMs, comments, and mentions, the Jotform Instagram Agent promises to save time while strengthening your connection with your audience. Curious how it adapts to your unique communication style or integrates with platforms beyond Instagram? Let's uncover how this AI redefines what it means to stay engaged in the digital age. Sometimes, the best way to be present is to let technology amplify your voice.

"AI represents a rare, once-a-decade opportunity to rethink what a browser can be," OpenAI's CEO said yesterday when announcing the company's latest product: ChatGPT Atlas. In this new AI-powered browser, ChatGPT becomes the central mechanism for surfing the internet. From any webpage in Atlas, you can click an "Ask ChatGPT" button to open a side conversation with the chatbot. Want cooking inspiration? Atlas can pull from recipes you've recently viewed through its "browser memories" feature.

LangChain raised $125 million at a $1.25 billion valuation, the company announced on Monday. TechCrunch reported in July that the provider of a popular open source framework for building AI agents was raising fresh funds at a valuation of at least $1 billion. The deal was led by IVP, as we previously reported. New investors CapitalG and Sapphire Ventures joined in, as did existing investors Sequoia, Benchmark, and Amplify.

"They just don't work. They don't have enough intelligence, they're not multimodal enough, they can't do computer use and all this stuff," he said. "They don't have continual learning. You can't just tell them something and they'll remember it. They're cognitively lacking and it's just not working."

The American dream is "life, liberty, and the pursuit of happiness" but, in practice, it has always been about ownership. Sadly, the dream of ownership is slowly slipping away for many people. Harvard University's 2025 Youth Poll found that three-quarters say they want to own a home, but barely half think they ever will. Ownership feels increasingly out of reach.

A vertical agent is more than a chatbot. It's a core part of the martech stack, built with autonomy, context and memory to drive business goals. Vertical agents are powered by the LLM of choice but are trained on a company's catalogs, knowledge base, policies, and brand tone - all centralized in a unified data source. They: Embody the roles a brand requires (i.e., sales, support, etc.). Understand industry language. Adapt across multiple languages. Deliver credible responses.

Problem: If your pricing is tied to human users, but AI is doing the work, you're leaving money on the table (or worse, annoying customers with irrelevant seat counts). Reality: Customers don't care about seats. They care about results. Manny's take: "Don't sell software. Own outcomes." If your product helps a customer resolve 1,000 support tickets a month, why charge for seats? Charge for resolved tickets.

Around the middle of last year, Pim de Witte started reaching out to a handful of prominent AI labs to see if they'd be interested in using data from Medal, his popular video game clipping platform, to train their agents. Within weeks, it became clear that Medal's data was more valuable to the labs than he expected. "We received multiple acquisition offers very quickly," he told me.

Regarding AI agents, the survey found ambition was outpacing readiness. Overall, 83% of organisations planned to deploy AI agents, and nearly 40% expected them to work alongside employees within a year. But the study discovered that, for majority of these companies, AI agents were exposing weak foundations - that is, systems that can barely handle reactive, task-based AI, let alone AI systems that act autonomously and learn continuously.

A research team from Stanford University has released Paper2Agent, a framework that automatically converts scientific papers into interactive AI agents. The system, introduced in a recent paper, aims to make research methods more accessible by transforming traditional publications into dynamic entities that can execute analyses, reproduce results, and respond to new scientific queries through natural language interaction. Paper2Agent builds on the Model Context Protocol (MCP), a standard that allows large language models to connect with external tools and datasets.

In an ever-changing world of U.S. tariffs, shifting trade policies, and rising geopolitical tensions, businesses are forced to make decisions at an expedited pace. AI is here to help: streamlining some productivity and allowing businesses and their leaders to gather and summarize information at a faster clip. That's why Hanneke Faber, the CEO of global tech manufacturing company Logitech, said she'd be open to the idea of having an AI-powered board member.

However, AWS hasn't just made another drag-and-drop agent builder. The e-commerce giant is also using generative AI models to help users plan out and create automated workflows that take advantage of tools such as LLMs in a matter of minutes. For example, Amazon's Quick Flows is designed to automate routine tasks by allowing the user to explain what they're trying to accomplish and what the deliverable should look like. Meanwhile, Amazon's Quick Automate is similar in concept but is designed to support more complex projects.

That's where Knapsack comes in. It's a collaboration platform specifically designed for enterprises that need to resolve misunderstandings between UI designers, product managers, and engineers. Knapsack creates a unified workspace that connects with tools like Figma and Git, ensuring that any design changes are automatically updated in the code and documentation. This approach makes sure that everything remains up to date, so branding stays consistent across all digital products.

Slack believes it has a gold mine of data. According to the company, the conversation data between employees is said gold needed to feed AI with the right context. That data is now available within Agentforce, but also to third parties. In recent years, there has been a race to build the best LLMs and have sufficient computing power to enable AI. The latter has been achieved, and now it is time to collect the right data and context and feed it to AI agents.

Atlassian wants to help customers in multiple ways with Rovo AI. There is Rovo Search, Rovo Chat, and Rovo Studio. Today, these three areas are naturally linked to a growing range of AI agents. The search function searches more than 50 connected apps, from Jira and Confluence to external tools. The chat function uses company data to answer questions. In Rovo Studio, teams can build their own agents. This is done in plain language, without programming.

All those (AI) agents need management, automation, scalability control, maintenance, testing and orchestration. This was the central remit that CamundaCon set out to explore. Camunda's process orchestration and automation conference was held this month at the Sheraton New York Times Square Hotel. With end-to-end orchestration in its sights, did the company manage to deliver on its promise of "AI with no BS" (as the banner read on the hotel exterior) or would this event fuel more of the AI hype-cycle?

The $100T world of B2B commerce operates in a "Wild West" where businesses provide goods and services first and chase payment later - often for months. Despite decades of software innovation, the accounts receivable process still depends on millions of finance professionals manually sending emails, tracking down contacts, answering invoice questions, and reconciling incomplete payment data. With 57% of invoices paid late and 77% of AR teams falling behind, this communication and negotiation bottleneck has become one of the most persistent inefficiencies in the modern economy.

Hi everyone, my name is Srini Penchikala. I am the lead editor for AI, ML and data engineering community at infoq.com website and I'm also a podcast host. Thank you for tuning into this podcast. In today's episode, I will be speaking with Elena Samuylova, co-founder and CEO at Evidently AI, the company behind the tools for evaluating, testing and monitoring the AI powered applications.

The launches see new agents designed to make human-AI collaboration a reality; an operating system for collaboration devices with RoomOS 26, powered by Nvidia, to deliver agentic capabilities for users and IT; a Microsoft Device Ecosystem Platform (MDEP) to enhance security on Cisco devices running Microsoft Teams Rooms; and Webex Suite integrations including Amazon Q index, Microsoft 365 Copilot and Salesforce for agentic workflow automation.

What if you could delegate your most time-consuming tasks to a team of tireless, hyper-efficient assistants, all without hiring a single person? Enter Perplexity Comet, an innovative AI-powered browser that's reshaping how professionals approach their daily workflows. With its ability to handle everything from real-time competitor analysis to SEO optimization, this tool doesn't just automate, it transforms. Imagine a world where your research organizes itself, your sales leads find you, and your content practically writes itself.