#ai-agents

#ai-agents

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.

You ask Claude Code or Cursor about a shadcn/ui component, and it'll confidently spit out props that don't exist, dust off patterns from 2023, or just flat-out make things up. Most of the time, this comes down to version changes. shadcn/ui keeps evolving, new props, updated requirements, and agents often lean on older docs or outdated patterns. Other times, it's simply the AI guessing.

Identity and access management (IAM) provider Descope has announced raising $35 million in a seed funding extension that brings the total raised by the company to $88 million. The cash infusion came from existing investors Cerca Partners, Dell Technologies Capital, Lightspeed Venture Partners, Notable Capital, Triventures, and Unusual Ventures. Founded in 2022, the Los Altos, California-based startup landed a large $53 million seed funding round in 2023, aiming to disrupt the customer identity and authentication market.

The picture the company paints of the modern office worker is someone who's juggling a growing number of AI systems -- as more get added, it becomes increasingly difficult to manage them all. Instead, ServiceNow is offering to help by telling those workers to simply drop the juggling act and let their own AI software pick up where they left off.

Life sciences leaders are increasingly adopting AI and AI agents to address growing industry disruption. This shift is occurring as the sector confronts new regulatory demands that strain compliance teams, increasingly complex clinical trials, and rising expectations from healthcare professionals. A recent Salesforce study revealed that life sciences leaders see AI as a powerful tool for navigating these challenges, with 94% expecting AI agents to be critical for scaling organizational capacity and strengthening operations.

AI agents are steadily becoming embedded in enterprise workflows: automating customer interactions, coordinating operations, and reasoning across complex datasets. However, if you take a closer look beneath the surface, many organizations are struggling with the technical challenge of supporting them in real time. Legacy data architectures aren't built for this. To make agents performant, scalable, and accountable, IT leaders are turning to something familiar, but more flexible: NoSQL.

Far from futuristic hype, AI agents are now taking on the unglamorous but critical jobs: routing tickets, handling repetitive requests, surfacing the right information at the right moment, and keeping operations flowing with less human hand-holding. A McKinsey study says that, with agents in the mix: Up to 80% of common incidents could be resolved autonomously, with a reduction in time to resolution of 60 to 90%.

As Alex Kantrowitz of Big Technology likes to say, what often gets lost in these debates is the nuance - so let's parse the rhetoric from reality. At the extremes, two scenarios could play out: Firstly, in a world where AI agents facilitate critical moments of truth for consumers making decisions about brands, products, and services - and immediately execute transactions - advertising becomes obsolete.

"AI agents are a powerful new identity type. They can act independently, on their own or on behalf of a user or a team or a company," said McKinnon. "They can access tools, apps or data, they can plan or complete tasks on their own. The pace here of innovation is absolutely stunning. "These AI agents and the potential here, are getting very, very powerful and it's happening very quickly. "Without identity security AI security collapses. AI security is identity security, you can't be successful in one without the other."

Emergent, a company built by twin brothers Mukund and Madhav Jha, aims to become a similar platform for consumers when it comes to app creation. The platform allows non-technical users to create an app using prompts. While that's not a unique pitch in 2025, Emergent intends to aid users through the app development process, while also managing different APIs and deployment steps so they don't have to worry about various technicalities.

For years, Big Tech CEOs have touted visions of AI agents that can autonomously use software applications to complete tasks for people. But take today's consumer AI agents out for a spin, whether it's OpenAI's ChatGPT Agent or Perplexity's Comet, and you'll quickly realize how limited the technology still is. Making AI agents more robust may take a new set of techniques that the industry is still discovering.