Japan's internal affairs industry may introduce artificial intelligence agents at local governments facing labor shortages. Japan's internal affairs ministry has begun considering the introduction of artificial intelligence agents to autonomously perform tasks at local governments facing labor shortages. On Thursday, the ministry held the first meeting of a related study group consisting of relevant experts and local government officials to discuss which tasks could be assigned to AI agents and how local government employees could manage them. The group will compile an interim report by the end of fiscal 2026 and aim to release its final report around summer 2027. According to the ministry, 74% of the country's local governments were using AI in some form as of October 2025. While AI tools to classify data and make predictions and those to generate text and images based on prompts were commonly used, AI agents were rarely used, except in trials at some organizations.

Discover four foundational elements of AI architecture that will endure as models continue to advance: data quality, context engineering, governance, and human expertise. With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture--the structural framework required for deploying and managing reliable, integrated AI systems at scale--allows technology leaders to make astute decisions today while supporting a future of AI agents that can retrieve information, make decisions, and execute complex workflows across systems. The following capabilities provide a stable compass on the path to production-ready deployment, regardless of how the underlying technology evolves. Models are only as reliable as the data they can access, and poor data quality leads to AI hallucinations, bias, and unreliable outputs.

PCWorld explores the risks and benefits of using AI agents like Claude for email management, following Notion Mail's recent shutdown that left users dependent on AI sorting. AI email automation offers appealing benefits including reduced inbox clutter and better organization, but poses operational risks like misfiling or accidental deletion. Privacy concerns remain significant as AI agents access sensitive personal and financial data, requiring users to carefully weigh convenience against potential security risks. When I learned that Notion, the popular online workspace service, was shutting down its Notion Mail product, it wasn't the shutdown itself that got my attention. No, it was this: because so many Notion users had handed over their email sorting duties to AI agents, they'd stopped bothering to open their inboxes . Letting AI agents sort through all your email has long been considered a killer app for AI, although the convenience doesn't come without some serious risks.

Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated training data, or, when the generator runs unsupervised, reward it by a difficulty heuristic that need not improve the solver. We introduce INFUSER, an iterative co-training framework with two co-evolving roles: a Generator that drafts questions and reference golden answers from a pool of unstructured, automatically collected documents, and a Solver that improves by training on them. The solver is trained with standard correctness rewards against the generator-provided answers, while the generator is rewarded by an optimizer-aware influence score that measures whether each proposed question would actually improve the solver on the target distribution. Because this continuous, noisy influence score is poorly served by standard GRPO, we propose DuGRPO, a dual-normalized variant of GRPO, for generator training. Together, these turn the document pool into an adaptive curriculum that favors questions useful to the current solver, not just hard ones. On Qwen3-8B-Base, INFUSER outperforms strong self-evolution baselines with over 20% relative improvement on Olympiad and SuperGPQA benchmarks, and an 8B INFUSER co-evolving generator outperforms a frozen 32B thinking generator on math and coding. Ablations confirm each design choice is necessary, and two extensions, applying INFUSER to an instruction-finetuned anchor and augmenting it with rule-verifiable RLVR data, further demonstrate the flexibility and generalizability of the framework. Code is available at https://github.com/FFishy-git/INFUSER.

Plus: The US House has passed new youth online safety legislation. AI agents are not your "coworkers" Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool--one that your company nonetheless calls Alex, an "employee" with a title and defined responsibilities. How well do you think you would work with Alex? If you're anything like the managers studied by Boston University professor Emma Wiles, treating that AI as a coworker would lead you to do a worse job. They caught 18% fewer errors when the work was attributed to an agentic AI employee rather than a chatbot. This is an alarming glimpse of the future Silicon Valley is hurling us toward.

AI agents are not your "coworkers" Marketing AI agents as digital employees may make human workers worse at spotting errors and more likely to offload accountability. Imagine coming in to work to learn that a new underling will report to you. The worker is not a person but an AI tool--one that your company nonetheless calls Alex, an "employee" with a title and defined responsibilities. How well do you think you would work with Alex? If you're anything like the managers recently studied by Emma Wiles, a Boston University business professor, treating Alex as a "coworker" and not a software tool would lead you to do a worse job. Wiles found that people caught 18% fewer errors when the work was said to have come from an agentic "AI employee" rather than a chatbot. It turns out that what's in a name matters.

A ranking of 101 agent tasks reveals where workflows are trending and where connected intelligence is critical. Enterprise investment in AI is booming. Gartner is calling 2026 an " inflection year " for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek. A prime opportunity for AI agents exists in the tech function, where IT infrastructure costs are projected to grow two to three times by 2030, even as budgets remain unchanged, according to McKinsey . And in the last 18 months, tech teams--the engineers, developers, architects, and other practitioners who are building, deploying, and continually improving their organizations' infrastructure and applications--are clearly putting agents to work.

Recent advancements have increasingly focused on leveraging large language models (LLMs) to construct autonomous agents for complex problem-solving tasks. However, existing approaches predominantly employ a single-agent framework to generate search branches and estimate rewards during Monte Carlo Tree Search (MCTS) planning. This single-agent paradigm inherently limits exploration capabilities, often resulting in insufficient diversity among generated branches and suboptimal planning performance.

When you purchase through links in our articles, we may earn a small commission. Run private, reliable local AI workflows with MSI's Cubi NUC AI+ 3MG mini PC, built for compact on-device AI, agents, and secure automation. Agentic AI is the current buzzword of the day, but if you're chatting with ChatGPT, having Gemini make images for you, or coding with Claude, then you're almost exclusively interacting with a cloud AI ecosystem. This is great for the most capable, frontier models, but it's dependent on server uptime, gives few guarantees for privacy and security, and once you start running agents, token costs can quickly run away from you. For smaller, everyday AI tasks, persistent agentic work, or partnering with more powerful cloud or local AI tools, small-scale local AI PCs can be ideal.

Multi-agent reinforcement learning (MARL) has emerged as a powerful framework for modeling autonomous agents that independently optimize their individual objectives. However, in mixed-motive MARL environments, rational self-interested behaviors often lead to collectively suboptimal outcomes situations commonly referred to as social dilemmas. A key challenge in addressing social dilemmas lies in accurately quantifying and representing them in a numerical form that captures how self-interested agent behaviors impact social welfare. To address this challenge, \textit{externalities} in the economic concept is adopted and extended to denote the unaccounted-for impact of one agent's actions on others, as a means to rigorously quantify social dilemmas.