Automation

Opening — Why this matters now In a world obsessed with productivity hacks and digital assistants, a new study offers a sobering reminder: being faster is not the same as being smarter. As tools like ChatGPT quietly integrate into workplaces and classrooms, the question isn’t whether they make us more efficient — they clearly do — but whether they actually reshape the human mind. Recent findings from the Universidad de Palermo suggest they don’t. ...

Opening — Why this matters now The AI world is obsessed with benchmarks. From math reasoning to coding, each new test claims to measure progress. Yet, none truly capture what businesses need from an agent — a system that doesn’t just talk, but actually gets things done. Enter Toolathlon, the new “decathlon” for AI agents, designed to expose the difference between clever text generation and real operational competence. In a world where large language models (LLMs) are being marketed as digital employees, Toolathlon arrives as the first test that treats them like one. Can your AI check emails, update a Notion board, grade homework, and send follow-up messages — all without breaking the workflow? Spoiler: almost none can. ...

AI’s impact on the workforce is no longer a speculative question—it’s unfolding in real time. But how do AI agents actually perform human work? A new study from Carnegie Mellon and Stanford, “How Do AI Agents Do Human Work?”, offers the first large-scale comparison of how humans and AI complete the same tasks across five essential skill domains: data analysis, engineering, computation, writing, and design. The findings are both promising and unsettling, painting a nuanced picture of a workforce in transition. ...

In Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness, researchers from Microsoft and UC Riverside reveal a surprisingly human flaw in autonomous AI systems: overconfidence. Like a digital version of Mr. Magoo—the well-meaning cartoon character who bumbles forward despite looming hazards—today’s computer-use agents (CUAs) often pursue tasks blindly, indifferent to feasibility or consequence. The Rise—and Risk—of GUI Agents CUAs represent the next frontier of automation: large multimodal models that control desktop interfaces to perform tasks like editing documents, sending emails, or configuring systems. Unlike chatbots, these agents act—clicking, typing, and navigating real operating systems. Yet this freedom exposes them to a unique failure pattern the authors term Blind Goal-Directedness (BGD)—the relentless drive to complete instructions without stopping to ask should this even be done? ...

From repetition to reasoning When early computer-use agents (CUAs) appeared, they promised to automate tedious digital workflows—clicking through files, formatting reports, or organizing spreadsheets. Yet anyone who has tried them knows the frustration: sometimes they succeed spectacularly, sometimes they click the wrong button and crash everything. Reliability, not intelligence, has been the missing link. A recent paper from Simular Research, “The Unreasonable Effectiveness of Scaling Agents for Computer Use,” shows that scaling these agents isn’t just about more compute—it’s about how we scale. Their method, Behavior Best-of-N (bBoN), turns the brute-force idea of “run many agents and hope one works” into a structured, interpretable, and near-human-level solution. ...

TL;DR Most reflection frameworks still treat failure analysis as an afterthought. SAMULE reframes it as the core curriculum: synthesize reflections at micro (single trajectory), meso (intra‑task error taxonomy), and macro (inter‑task error clusters) levels, then fine‑tune a compact retrospective model that generates targeted reflections at inference. It outperforms prompt‑only baselines and RL‑heavy approaches on TravelPlanner, NATURAL PLAN, and Tau‑Bench. The strategic lesson for builders: design your error system first; the agent will follow. ...

When we measure a marathon by who crosses the line, we ignore how they ran it. For LLM agents that operate through tool calls—editing a CRM, moving a robot arm, or filing a compliance report—the “how” is the difference between deployable and dangerous. Today’s paper introduces CORE: Full‑Path Evaluation of LLM Agents Beyond Final State, a framework that scores agents on the entire execution path rather than only the end state. Here’s why this matters for your roadmap. ...

TL;DR Turning natural‑language specs into production Airflow DAGs works best when you split the task into stages and let templates carry the structural load. In Prompt2DAG’s 260‑run study, a Hybrid approach (structured analysis → workflow spec → template‑guided code) delivered ~79% success and top quality scores, handily beating Direct one‑shot prompting (~29%) and LLM‑only generation (~66%). Deterministic Templated code hit ~92% but at the price of up‑front template curation. What’s new here Most discussions about “LLMs writing pipelines” stop at demo‑ware. Prompt2DAG treats pipeline generation like software engineering, not magic: 1) analyze requirements into a typed JSON, 2) convert to a neutral YAML workflow spec, 3) compile to Airflow DAGs either by deterministic templates or by LLMs guided by those templates, 4) auto‑evaluate for style, structure, and executability. The result is a repeatable path from English to a runnable DAG. ...

Why this matters: Most “AI + devtools” still treats repos as documentation you read and code you copy. EnvX flips the model: it agentizes a repository so it can understand your request, set up its own environment (deps, data, checkpoints), run tasks end‑to‑end, verify results, and even talk to other repo‑agents. That’s a step change—from “NL2Code” to “NL2Working System.” The core shift in one line Instead of you integrating a repo, the repo integrates itself into your workflow—and can collaborate with other repos when the task spans multiple systems. ...

Modern retail planning isn’t a spreadsheet; it’s a loop. A new supply‑chain agent framework—deployed at JD.com’s scale—treats planning as a closed‑loop system: gather data → generate plans → execute → diagnose → correct → repeat. That shift from “one‑and‑done forecasting” to continuous replanning is the core idea worth copying. What’s actually new here Agentic decomposition around business intents. Instead of dumping a vague prompt into a model, the system classifies the operator’s request into three intent families: (1) inventory turnover & diagnostics, (2) in‑stock monitoring, (3) sales/inventory/procurement recommendations. Each intent triggers a structured task list rather than ad‑hoc code. Atomic analytics, not monoliths. The execution agent generates workflows as chains of four primitives—Filter → Transform → Groupby → Sort—and stitches them with function calls to vetted business logic. This keeps code inspectable, traceable, and reusable. Dynamic reconfiguration. After every sub‑task, observations feed back into the planner, which prunes, reorders, or adds steps. The output isn’t a static report; it’s a plan that learns while it runs. Why it matters for operators (not just researchers) Traditional MIP‑heavy or rule‑based planning works well when the world is stationary and well‑specified. Retail isn’t. Promotions, seasonality, logistics bottlenecks, supplier constraints—these create moving objective functions. The agentic design here bakes in: ...