Automation

From Static Scripts to Living Workflows The AI agent world has a scaling problem: most automated workflow builders generate one static orchestration per domain. Great in benchmarks, brittle in the wild. AdaptFlow — a meta-learning framework from Microsoft and Peking University — proposes a fix: treat workflow design like model training, but swap numerical gradients for natural language feedback. This small shift has a big implication: instead of re-engineering from scratch for each use case, you start from a meta-learned workflow skeleton and adapt it on the fly for each subtask. ...

The AI Pressroom Arrives — Mostly Unannounced When ChatGPT-3.5 launched in late 2022, it didn’t just disrupt classrooms and coding forums — it quietly walked into newsrooms. A recent large-scale study of 40,000+ news articles shows that local and college media outlets, often operating with lean budgets and smaller editorial teams, have embraced generative AI far more than their major-network counterparts. And in many cases, readers have no idea. The research, spanning opinion sections from CNN to The Harvard Crimson, and across formats from print to radio, found a tenfold jump in AI-written local news opinion pieces post-GPT. College newspapers followed closely with an 8.6× increase, while major outlets showed only modest uptake — a testament to stricter editorial controls or more cautious adoption policies. ...

A Hidden Cost of AI Efficiency When AI takes over routine tasks, companies often see immediate productivity gains. Senior staff can accomplish more without relying on juniors, costs go down, and short-term profits rise. But beneath these benefits lies a risk that most boardrooms overlook: the erosion of tacit knowledge—the hands-on expertise that only develops through years of guided practice. Tacit skills aren’t in manuals or knowledge bases. They’re the intuition of a surgeon who adapts mid-procedure, the judgment of a lawyer during negotiations, the troubleshooting instincts of an engineer. These skills pass from experts to novices mainly through direct collaboration on real work. Remove the entry-level work, and you cut the ladder that builds tomorrow’s experts. ...

In enterprise AI automation, the devil isn’t in the details—it’s in the dependencies. As LLM-powered agents gain access to hundreds or thousands of external tools, they face a simple but costly problem: finding all the right tools for the job. Most retrieval systems focus on semantic similarity—matching user queries to tool descriptions—but ignore a crucial fact: some tools can’t work without others. The result? A task that seems perfectly matched to a retrieved tool still fails, because a prerequisite tool never made it into the context window. Tool Graph Retriever (TGR) aims to solve this by making dependencies first-class citizens in retrieval. ...

The idea of software that writes software has long hovered at the edge of science fiction. But with the rise of LLM-based code agents, it’s no longer fiction, and it’s certainly not just autocomplete. A recent survey by Dong et al. provides the most thorough map yet of this new terrain, tracing how code generation agents are shifting from narrow helpers to autonomous systems capable of driving the entire software development lifecycle (SDLC). ...

Imagine asking, “Why wasn’t Order A scheduled for production yesterday?” and getting not just an answer, but a causal breakdown, an alternative plan, and a visual comparison — all without involving your operations research (OR) consultant. That’s exactly what SMARTAPS delivers. Built by Huawei researchers, SMARTAPS is a tool-augmented LLM interface for interacting with Advanced Planning Systems (APS) using natural language. It doesn’t try to replace optimization solvers — it simply makes them accessible. In doing so, it redefines how planners interact with complex decision-making models. ...

Scientific communication has always suffered from the tyranny of static text. Even the most revolutionary ideas are too often entombed in dense LaTeX or buried in 30-page PDFs, making comprehension an uphill battle. But what if your next paper—or internal training doc—could explain itself through animation? Enter Manimator, a new system that harnesses the power of Large Language Models (LLMs) to transform research papers and STEM concepts into animated videos using the Manim engine. Think of it as a pipeline from paragraph to pedagogical movie, requiring zero coding or animation skills from the user. ...

As LLM-powered agents become the backbone of many automation systems, their ability to reliably invoke external tools is now under the spotlight. Despite impressive multi-step reasoning, many such agents crumble in practice—not because they can’t plan, but because they can’t parse. One wrong parameter, one mismatched data type, and the whole chain collapses. A new paper titled “Butterfly Effects in Toolchains” offers the first systematic taxonomy of these failures, exposing how parameter-filling errors propagate through tool-invoking agents. The findings aren’t just technical quirks—they speak to deep flaws in how current LLM systems are evaluated, built, and safeguarded. ...

In an age where aging infrastructure meets accelerating AI, a new paper out of George Mason University proposes a novel question: Can large language models interpret what even seasoned engineers find difficult — NDE contour maps of bridges? The answer, based on this pilot study, is a cautious but resounding yes — with caveats that echo through the entire field of AI-assisted engineering. The Problem: Data Is There — Expertise Isn’t Always Bridges are scanned using advanced non-destructive evaluation (NDE) tools — Ground Penetrating Radar (GPR), Electrical Resistivity (ER), Impact Echo (IE), and Ultrasonic Surface Waves (USW) — but interpreting those outputs requires human expertise, which is not always available, especially during emergency assessments or in rural areas. Contour maps from these tools don’t speak for themselves. ...

Most real-time financial systems rely on deep stacks of infrastructure, from custom APIs to cloud VMs and high-frequency data ingestion pipelines. But what if a single developer could deploy a daily-updating, AI-powered stock analysis engine without a single server? That’s exactly what Taniv Ashraf set out to do — and accomplished — in his recent case study on a fully serverless architecture using Google Gemini, GitHub Actions, and static web hosting. The result is an elegantly simple yet conceptually powerful demonstration of how qualitative LLM analysis and automation tools can replace entire categories of financial tooling — if wielded strategically. ...