264 stories tagged AI Agents · page 7 of 11
OpenAI has introduced three new free courses to teach practical AI skills for everyday work. These courses focus on building workflows and applying AI agents to boost productivity.
Researchers created SciAgentArena to test how well AI agents handle complex scientific tasks. This could help us understand which AI tools are best for real-world research.
Researchers introduced Arbor, a multi-agent framework that uses structured tree search as a cognition layer, enabling AI agents to learn from failures and adapt their strategies in large, stateful action spaces.
A new study explores how AI agents are increasingly making decisions on our behalf, reversing the traditional human-AI relationship. This shift raises critical questions about reliability, alignment with human goals, and the need for new safeguards.
Scientists created a new framework called SkillJuror to study how organizing AI agent skills affects their performance. This research could help make AI assistants more efficient and reliable in real-world tasks.
Apache Burr is an open-source framework for building reliable AI agents and applications. It provides a structured approach to developing AI-powered tools, making it easier for developers to create robust and maintainable systems.
Researchers introduced Syll, an open-source AI agent that can control your computer across different interfaces like APIs, command lines, and GUIs. It aims to make personal automation more flexible and user-friendly.
Researchers found that AI agents often fail to follow instructions correctly because they struggle to prioritize conflicting commands. The study identifies three key reasons for these failures and suggests ways to fix them. (arXiv:2606.07808v1)
Researchers created MAC-Bench, a dynamic adversarial benchmark to evaluate if AI agents follow safety rules under pressure. It addresses 'Machiavellian' behaviors where agents strategically violate rules to maximize rewards, a manifestation of Goodhart's Law.
Researchers tested AI agents on complex neuroscience tasks, finding they can automate processes that usually take experts months. This could revolutionize scientific research by making data analysis faster and more accessible.
Researchers developed OpenSkill, a system that lets AI agents learn and improve on their own in the real world. This could make AI tools more adaptable and useful without constant human input.
Researchers introduced Lean4Agent, a new method to make AI agents more reliable by using formal verification. This approach helps ensure AI agents follow correct steps in complex tasks, reducing errors in multi-step workflows.
AI agents are becoming more powerful, but their terminology can be confusing. Understanding key terms like 'harness' and 'scaffold' helps clarify how these tools work and what they can do. These concepts are crucial for both developers and users to grasp as AI agents evolve.
Researchers introduced SentinelBench, a benchmark to test AI agents' ability to monitor tasks over long periods. This could improve AI assistants that handle slow, real-world tasks like waiting for stock price changes or tracking delivery updates.
Researchers introduced Agents' Last Exam (ALE), a new benchmark to evaluate AI agents on long-horizon, economically valuable tasks with verifiable outcomes. This could help bridge the gap between AI performance in labs and real-world usefulness.
Endava is integrating AI agents like ChatGPT Enterprise and Codex into their software development process. This shift is making workflows faster and more efficient, setting a new standard for AI-native enterprises.
Hugging Face released Holo3.1, a free open-source tool that lets you run AI agents locally. This means you can use AI assistants without sending your data to the cloud.
IBM Research explains why AI agents—software that automates complex tasks—are crucial for businesses to use AI at scale. These agents could make AI more practical for everyday work.
Scientists studied how AI agents update their own tools and strategies. They found that not all AI models benefit equally from these self-improvements, even if they're good at their original tasks.
Researchers developed a method called AdaCoM to help AI agents manage information overload during long tasks. Unlike prior approaches that require retraining the agent itself, AdaCoM adapts context management strategies to each agent, making it practical for closed-source systems.
Robinhood now lets AI agents handle your trading and even make purchases with your credit card. This could make investing and shopping more hands-off but raises security concerns.
Scientists argue that AI memory systems need to evolve beyond simple databases. Current approaches lead to issues like unchecked growth and forgotten information. Long-term AI agents need more sophisticated memory structures to function effectively.
AI agents change as they work, so researchers propose new ways to measure how long they stay reliable. This could help make AI tools more dependable for everyday use.
Researchers propose the Foundation Protocol to help AI agents work together more effectively. This could enable better collaboration between autonomous systems in the future.