Automation

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: ...

Most “agent” decks promise autonomy; few explain how to make it shippable. A new survey of LLM‑based agentic reasoning frameworks cuts through the noise with a three‑layer taxonomy—single‑agent methods, tool‑based methods, and multi‑agent methods. Below, we translate that map into a practical build/run playbook for teams deploying AI automation in real workflows. TL;DR Single‑agent = shape the model’s thinking loop (roles, task prompts, reflection, iterative refinement). Tool‑based = widen the model’s action space (APIs, plugins/RAG, middleware; plus selection and orchestration patterns: sequential, parallel, iterative). Multi‑agent = scale division of labor (centralized, decentralized, or hierarchical; with cooperation, competition, negotiation). Treat these as orthogonal dials you tune per use‑case; don’t jump to multi‑agent if a reflective single agent with a code‑interpreter suffices. 1) What’s genuinely new (and useful) here Most prior surveys were model‑centric (how to finetune or RLHF your way to better agents). This survey is framework‑centric: it formalizes the reasoning process—context $C$, action space $A = {a_{reason}, a_{tool}, a_{reflect}}$, termination $Q$—and shows where each method plugs into the loop. That formalism matters for operators: it’s the difference between “let’s try AutoGen” and “we know which knob to turn when the agent stalls, loops, or hallucinates.” ...

The gist (and why it matters for business) Enterprise buyers don’t reward demos; they reward repeatable completions per dollar. ComputerRL proposes a path to that by (1) escaping pure GUI mimicry via a machine-first API-GUI action space, (2) scaling online RL across thousands of Ubuntu VMs, and (3) preventing policy entropy collapse with Entropulse—a cadence that alternates RL and supervised fine-tuning (SFT) on successful rollouts. The result: a reported 48.1% OSWorld success with markedly fewer steps than GUI-only agents. Translation for buyers: lower latency, lower cost, higher reliability. ...

TL;DR Classical planners crack under scale. You can rescue them with LLMs in two ways: (1) Inspire the next action, or (2) Predict an intermediate state and split the search. On diverse benchmarks (Blocks, Logistics, Depot, Mystery), the Predict route generally solves more cases with fewer LLM calls, except when domain semantics are opaque. For enterprise automation, this points to a practical recipe: decompose → predict key waypoints → verify with a trusted solver—and only fall back to “inspire” when your domain model is thin. ...

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. ...