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In a recent research paper, Google introduced Beyond Zero, a “security model for the AI era” that extends Zero Trust to autonomous AI agents. The new approach moves access decisions from the application level to individual resources and actions, combining static authorization controls with dynamic AI-driven decisions to enable machine-speed enforcement for humans and agents. By Renato Losio
The AI era has completely broken enterprise buying patterns, and startups haven't yet figured out how to navigate.
Enterprise resource planning has traditionally been associated with large vendors, complex implementations, long contracts, and significant licensing investments. For decades, companies evaluating ERP systems were likely to encounter names such as SAP, Oracle, Microsoft Dynamics, and other established commercial platforms. That model is not disappearing. But the assumptions behind it are changing. In 2026, enterprises have more choices than simply selecting between competing proprietary ERP vendors. Open-source platforms such as ERPNext, Odoo, and other business application ecosystems have become credible alternatives for organizations that want greater control over their software, more flexible customization, and different approaches to total cost of ownership. At the same time, SaaS pricing, vendor lock-in, integration complexity, cloud adoption, data ownership, and increasingly capable development tools are changing how businesses think about enterprise software. This does not mean open-source ERP is automatically better. It means the ERP decision deserves to be reconsidered. The question is no longer simply: Which ERP vendor should we buy from? Increasingly, organizations are asking: How much control should we retain over the software that runs our business? What Is Open-Source ERP? Open-source ERP is enterprise resource planning software whose source code is made available under an open-source license that grants defined rights to use, inspect, modify, and distribute the software. The practical implications depend heavily on the specific project's license. For an enterprise buyer, however, the important distinction is that open-source software can provide a level of visibility and extensibility that proprietary software may not. A simplified comparison looks like this: Proprietary ERP Business │ ▼ Vendor Software │ ├── Vendor controls source ├── Vendor controls roadmap ├── Vendor controls licensing └── Vendor controls many upgrade decisions Open-Source ERP Busi
When businesses outgrow spreadsheets and disconnected SaaS tools, the next question is often whether they should buy another application, customize an existing platform, or build a system specifically around their workflows. For many organizations, building custom business software can appear expensive and technically demanding. A development team has to think about authentication, permissions, database models, APIs, user interfaces, background jobs, reporting, audit trails, and deployment. This is where open-source application frameworks can change the equation. Instead of building every foundational capability from scratch, a framework can provide the underlying architecture while developers focus their effort on the business problems that actually differentiate the organization. One example is the Frappe Framework , an open-source web application framework used to build business applications such as ERPNext. But what exactly is Frappe, and why would an organization consider using it for custom enterprise software? What Is Frappe Framework? Frappe is an open-source, Python- and JavaScript-based web application framework designed to make it easier to build database-driven business applications. Rather than being simply a collection of programming utilities, Frappe provides a broader application foundation. It includes capabilities for: Data modeling Authentication Role-based permissions REST APIs Web forms Background jobs Reporting Workflow management Notifications File attachments Activity and audit information User interfaces Database access Application configuration This means a development team can start with an application architecture that already understands many of the requirements common to business software. The important distinction is this: Frappe is a framework for building applications. ERPNext is an application built using that framework. That distinction matters when evaluating Frappe for custom software development. Frappe vs ERPNext Frappe and ERP
I recently built a complete IT ticket management system in Power Apps — 9 screens, role-based access, live SLA tracking, and automatic email notifications. The part I want to actually talk about here isn't the UI, it's the SLA engine, because I built it to work without Power Automate , and the trick is simpler than it looks. The problem SLA tracking normally means: a ticket is "Critical" → 60 minute target → somebody needs to know if it's about to breach or already has. The obvious way to do this is a scheduled Power Automate flow that checks every ticket on a timer and flags the ones in trouble. I wanted this app to run on Power Apps collections only — no flow, no external data source — so a scheduled flow wasn't an option. The question was: can you get "live" SLA status without a background job? The trick: recalculate on every read, not on a timer Instead of a flow updating a SLAStatus field periodically, I recalculate it every time the app or a screen is opened, using Now() against the stored due date: \ UpdateIf( colTickets, Status <> "Resolved" && Status <> "Closed", { SLAStatus: If( Now() > DueDate, "Breached", DateDiff(Now(), DueDate, TimeUnit.Minutes) <= SLAMinutes * 0.2, "At Risk", "On Track" ) } ); UpdateIf(colTickets, Status = "Resolved" || Status = "Closed", {SLAStatus: "Met"}) \ \ This runs in App.OnStart , at the top of every screen's OnVisible , and behind a manual "Refresh SLA" button. The At Risk threshold is 20% of the SLA window remaining — so a Critical ticket (60 min target) goes At Risk with 12 minutes left; a Low ticket (1440 min / 24 hrs) goes At Risk with 4.8 hours left. The honest tradeoff: this only updates when someone has the app open. A ticket breaching at 2am with nobody looking won't trigger anything until the next visit. For a real production deployment I'd pair this with a scheduled flow for after-hours detection — but for a demo, an internal tool with regular traffic, or anything where "eventually consistent within the next visit"
Imagine an HR manager who has just completed this month’s hiring drive. Dozens of candidates have accepted their offers, and everyone is eager to onboard them as quickly as possible. But before that can happen, every background verification request has to move through multiple checks, vendors, approvals, and status updates. What should be a straightforward operational process often turns into hours of chasing spreadsheets, checking vendor portals, and answering the same status questions over and over again. It isn’t a technology problem as much as it is a visibility problem — and that’s the challenge that inspired BGVerifyIQ. The problem every HR team quietly struggles with Ask any HR or Talent Acquisition team what happens after an offer letter goes out, and you’ll hear a familiar story. A candidate is selected. A background verification (BGV) request is raised with a vendor. And then… the waiting begins. Behind that wait is a surprising amount of manual work: HR has to log into multiple vendor portals just to check where a case stands. Status updates live in scattered Excel sheets, emails, and vendor dashboards that rarely agree with each other. Nobody has a single, live number for “how many candidates are actually ready to be approved today.” SLA breaches — cases that have been open too long — get discovered late, usually when a hiring manager complains. When a discrepancy or a “suspect” flag shows up, it can get buried in a spreadsheet row instead of being escalated immediately. Every time leadership asks “how are our BGV vendors performing,” someone spends half a day pulling numbers together manually. New joiners get onboarded before their verification is even reviewed properly, simply because nobody had a clear, current view of their case. None of this is because HR teams aren’t diligent. It’s because the tools weren’t built for this specific job. Spreadsheets don’t talk to vendor systems. Vendor portals don’t talk to each other. And nobody has time to become
The acquisition also leaves Sequoia-backed Serval as the de facto startup leader in AI IT service automation, industry watchers believe.
Wonderful said it will use its $550 million Series C funding to develop products faster, expand its FDE teams, and meet demand for its products.
The startup wants to do for IT infrastructure what Cursor did for software engineering.
Microsoft 365 and Outlook are still seeing service degradations on Tuesday, the company's status page indicates.
Microsoft says it's testing a fix for the widespread Outlook issues that have led to email delays and failures.
Nvidia invests $3.5 billion into Taiwanese chipmaker MediaTek. The deal shows how Nvidia plans to stay essential to AI infrastructure as Big Tech begins to build its own AI chips.
Caterpillar has spent decades putting autonomous machines to work at remote mining sites. It's now bringing that experience to AI deployment.
Il rischio reale dell'AI enterprise non sono gli agenti autonomi. È la complessità tra di loro. Executive Briefing — Settembre 2026 Quando le aziende deployano fleet di agenti AI invece di sistemi singoli, il pericolo vero non è un agente che si mette a fare il matto da solo. È la complessità emergente delle loro interazioni: una ragnatela di chiamate a cascata, permessi dimenticati e gap di accountability che nessuna checklist può chiudere. 1. Il problema che nessuno vede arrivare Le aziende non deployano un agente e lo guardano girare. Deployano fleet: bot di supporto, agenti di retrieval, layer di orchestrazione, ognuno che chiama API, delega ad altri agenti, si infila in sistemi che non erano stati progettati per decisioni automatiche. Lo scenario che dovrebbe farvi perdere il sonno non è un singolo agente che combina un guaio. È cento agenti che fanno esattamente quello per cui sono stati costruiti, tutti insieme, in combinazioni che nessuno ha disegnato. La complessità non cresce linearmente col numero di agenti. Aggiungi un secondo agente e aggiungi una connessione. Aggiungi il decimo e potenzialmente aggiungi decine di connessioni, perché ora qualsiasi agente può chiamarne un altro, e ogni chiamata può scatenarne una terza altrove. Un ticket di supporto che prima toccava un solo sistema oggi può passare attraverso quattro agenti prima che un essere umano lo veda. E ogni passaggio è un punto decisionale non approvato. La maggior parte dei programmi AI enterprise si blocca quando gli umani responsabili perdono il filo. Chiedete a un team security quali agenti possono raggiungere quali sistemi, e otterrete silenzio. Chiedete quale agente ha triggered quale downstream action tre salti fa. Ancora silenzio. 2. Perché le checklist non funzionano L'istinto è trattarlo come compliance: approva l'agente, registralo, passa oltre. Ma una checklist valuta un singolo punto nel tempo. La complessità corre lungo una catena, e non puoi governare una catena con una pila di ap
Enterprise AI's real risk isn't autonomous agents. It's the complexity between them. Executive Briefing — September 2026 When enterprises deploy fleets of AI agents instead of single systems, the real danger is not a rogue agent. It is the emergent complexity of their interactions — a web of cascading calls, forgotten permissions, and accountability gaps that no checklist can fix. 1. The problem nobody sees coming Enterprises do not deploy one agent and watch it run. They deploy fleets: support bots, retrieval agents, orchestration layers, each calling APIs, delegating to other agents, reaching into systems that were never designed for machine decision-makers. The failure mode that should keep you up at night is not a single agent doing something bad. It is a hundred agents doing exactly what they were built to do, all at once, in combinations nobody designed for. Complexity does not grow linearly with agent count. Add a second agent and you add one connection. Add a tenth and you potentially add dozens, because any agent might call any other, and each call can trigger another somewhere else. A support ticket that used to touch one system might now pass through four agents before a human ever sees it. Every handoff is an undocumented decision point. Most enterprise AI programs stall when the humans responsible lose the thread. Ask a security team which agents can reach which systems, and you get silence. Ask which agent triggered which downstream action three hops ago. More silence. 2. Why checklists fail The instinct is to treat this like a compliance checklist. Approve the agent. Log the agent. Move on. But a checklist checks a single point in time. Complexity runs across a chain, and you cannot govern a chain with a stack of one-time approvals any more than you can call a diet successful because you had a vegetable once. Two failure modes dominate. Permissions creep. Somebody builds an agent to summarize support tickets and grants it broad API access because scop
Amazon is adding another 2 million Nvidia GPU chips to its data centers over the next two years. But this extended partnerships stretches beyond buying more chips.
Arga has raised $10 million in a seed funding round that was led by General Catalyst, with participation from Box Group, Emergence, Gradient and SV Angel.
Some fortnights the complaint is the bill. This one it was the product itself. Across the forums where paying customers of the big AI tools compare notes, the same grievance surfaced against three different companies in the same window, and it wasn’t about price at all. It was about direction : the new model feels worse than the old one, the app quietly took away the thing I used, and I can’t even tell what I’m running any more. Quotes sourced from: Reddit — specifically the subreddits r/ClaudeAI, r/cursor and r/perplexity_ai. Every quote below was opened at its permalink and copied verbatim; each is listed with its handle, subreddit and date in the Sources section. We quote experiences, not verdicts — a forum post is one person’s felt reality, and model quality is genuinely subjective, so we have framed these as exactly that: what it felt like to the person typing. “Rage-inducing”: the flagship that felt like a step back The sharpest thread came from Claude Code users trying, and failing, to get on with a new top-end model. A user posting as ronoudgenoeg opened it on 13 August with a title that set the tone — “Opus 5 is actually almost rage-inducing to use” — and a specific, un-nostalgic complaint: “Responses are way too verbose and buzzwordy and hard to follow. I legit don’t read 90% of the output anymore, that’s how bad it is. No matter what I put in my claude.md when it comes to communication style, after it did any type of meaningful work, it always reverts back to its extremely verbose, over-explained, buzzword heavy mess.” What made the thread notable wasn’t one angry post; it was the agreement, and how concrete it was. zimxero described asking the model to make a file more concise and getting “walls of text” and an hour of unwanted process in return. BeowulfShaeffer was blunter: “I fired opus 5. Worst model I’ve ever tried to use. I refuse to use it anymore.” And the tell that this was regression rather than grumbling — several users independently reaching f
Introduction & Industry Context The pursuit of operational efficiency has long been a cornerstone of enterprise strategy. For decades, Robotic Process Automation (RPA) served as the primary vehicle, automating repetitive, rule-based tasks across various departments. While RPA delivered initial gains, its inherent limitations—rigidity, high maintenance, and inability to handle ambiguity—are now becoming glaring bottlenecks in an increasingly dynamic business landscape. The digital era demands more than just automation; it requires hyper-automation: intelligent, adaptive systems capable of autonomous decision-making and continuous learning. This is precisely where the breakthrough of AI agents emerges, offering a paradigm shift from static, brittle automation to dynamic, resilient, and highly adaptable enterprise workflows. This blueprint outlines how CEOs and CTOs can strategically leverage modern AI agent orchestration to achieve unprecedented operational ROI. The Core Problem & Business/Technical Impact Traditional RPA solutions, while effective for strictly defined processes, struggle immensely with variability. Any deviation from a pre-programmed path, new data formats, or evolving business rules often leads to bot failures, requiring extensive human intervention and costly reprogramming. This rigidity manifests in several critical business impacts: Escalating Operational Costs: High maintenance overhead, constant recalibration, and the need for human exception handling negate much of the initial cost savings. Stifled Agility: Businesses cannot rapidly adapt to market changes or introduce new services when automation pipelines are inflexible. Missed Opportunities: Complex, unstructured data remains largely untouched by RPA, preventing deeper insights and value extraction. Human Resource Drain: Valuable human capital is trapped in mundane exception handling and bot maintenance, diverting focus from strategic initiatives. Hidden Tech Debt: A sprawling ecosystem of
Is the technique outdated? Yes. Is it still creepy? Also yes.