The AI Front Page

Search Edition

Search: rust

30 stories from 14 sources across 11 topics.

Stories

30

Sources

14

Topics

11

For You lens

27 stories in this edition match your reader profile.

Reader signals

3

Searches

0

Matches

27

Top score

149

Tune For You

Search Intent

rust

This query becomes a recent For You signal, so matching stories can move up on the next personalized pass.

For YouReader CenterPromote Search

Edition Index

Topic, entity, and source map

Topics

Agents3 storiesBig Tech3 storiesHot Takes3 storiesModels3 storiesOpen Source3 storiesPolicy3 stories

Entities

OpenAI5 storiesClaude4 storiesGemini3 storiesMeta3 storiesAnthropic2 storiesarXiv2 stories

Sources

Hacker News AI7 storiesBloomberg AI3 storiesThe Information AI3 storiesarXiv AI/ML2 storiesAWS Machine Learning Blog2 storiesGitHub Trending AI2 stories

The Information AI / 3:01 PM

Meta’s Muse Agent Almost Cost Me $408

• The Big Read: Investor Anjney Midha cut Anthropic an early check. His hot streak has kept going • Plus, Recommendations—our weekly pop culture picks: “ Our Town ,” “ Profits, Prophets, Coaches, and Kings ” and “ The Gentlemen ” A hotel room’s quality can be measured in terms of abundance: extra towels, spare bathrobes—plenty of Nespresso pods. Multiples of everything are almost always better. But what’s less pleasant is to inadvertently end up with multiple hotel rooms . A couple days ago, though, I found myself with the unfortunate opportunity to occupy a pair of accommodations at a Santa Monica, Calif., Marriott thanks to an error by my lil’ digital buddy: Muse, Meta Platforms’ new personal agent. The cumbersome, vexing tool hopes to seize on Silicon Valley’s zeitgeisty enthusiasm for popularizing autonomous AI. After a few days with it, I can’t imagine it’s the technology that will take agents mainstream. That hotel snafu came when I gave Muse the hotel’s name and asked it to book two nights. I plugged in my credit card details—a circuitous process that involved both Chase and Stripe—and was met with the digital equivalent of a shrug: An error message informed me that Muse hadn’t completed the transaction, and while it really didn’t know why it couldn’t, it assured me that my credit card hadn’t been charged. A little human skepticism led me to double-check its promise—good thing I did. In fact, the charge had gone through. What happened next still intrigues me. I gave Muse a screenshot showing the charge on my credit card and told it that the charge had gone through. Lickety-split, it returned with a Marriott confirmation number. Not bad—it finished the task without being instructed to do so. But somehow the AI managed to make two bookings: When I showed up to the hotel, I found two rooms waiting for me. The front-desk clerk showed some mercy and canceled one. Had a little human kindness not prevailed, I would’ve been out an extra $408, plus taxes and fees. I’d been pretty gung-ho to try Muse. We’ve spent the past year in increasingly complex discussions about agents ever since OpenClaw came out in January , marking what has felt like the beginning of a distinct new chapter in the AI era. Most of the agents that initially captured attention were meant to simplify work tasks. More recently, a couple of startups have captured buzz with ones devoted to improving life outside the office: The most talked-about one is from Instinct, a startup founded last year. (Instinct is so popular that it now faces a profound compute shortage, and it has recently been seeking additional fundraising that would value it at around $10 billion. Just a month ago, it was valued at $2.5 billion.) But Instinct isn’t widely available, so Muse felt like my first real chance to see what a personal agent could do. The recent reporting from my colleague Jyoti Mann made me even more curious to play around with it: Clearly, Meta sees Muse as a major new product , one aimed at the same everyday people who log on to Instagram and Facebook by the billions. Still, if their experiences are anything like mine, they’ll find Muse something of a misery. The hotel problem wasn’t the only hiccup I ran into with Muse. When I initially set up a account while on my work laptop using my phone number, I later couldn’t access it on my Mac mini at home or my iPhone: Login codes sent via text led me nowhere—just to more error messages. (Frankly, it’s a marvel I found the Muse app at all earlier this week, buried as it was below several other apps also named Muse in Apple’s App Store; it has since risen to the top.) Eventually, I caved and created a second account—this one linked to my Facebook account, which itself is tied to my phone number. Why couldn’t Muse pick up on the overlapping connections? I wish it could have. Later, I did get Muse to complete a Resy reservation and schedule an Uber. I can’t truthfully tell you it was faster or easier than if I’d just gone directly to those apps. After a while, I hit an existential conundrum. I’d given it the tasks that occurred to me, which numbered a small handful. What else could I get Muse to do? I’ve found interacting with the AI something like trying to wrangle a lackluster employee. If I wanted to maximize its potential, I’d need to think deeply and creatively about what else it could possibly do, coax it and baby it, then bite my nails and hope it actually carried out what I wanted. As I relate these frustrations, I can already hear someone shouting “Skill issue!” at me. But really, I stand by the conviction that mass-market consumer technology shouldn’t require any technical savvy or a lot of effort. Certainly, the best versions of such products do not—even the early versions. If three cars had routinely shown up each time someone ordered their very first Uber ride back in the 2010s, I promise you we’d see more yellow taxis on the road today and fewer Ubers. I don’t see agentic AI as some passing fad, nor am I hoping for such an outcome. The technology’s promise—to automate away some of life’s tedium—is damn alluring. What I expect will happen is that agentic AI will get woven into many existing apps, just as chatbots already populate the internet. People will make regular use of agentic software without ever really knowing it; Anthropic’s Claude, OpenAI’s ChatGPT and Google’s Gemini all already have some agentic capabilities. Obviously, Apple and Google will want to use the technology to make iPhone and Android phones smarter and more useful, and the agents will sync up with what those devices already know and store about us. That would reduce quite a few hurdles to agentic AI. But will new stand-alone apps like Muse take off? I have my doubts—not unless they get much, much simpler and more reliable. Or maybe I’ll just need to learn to see the silver lining in their mistakes. You know what? If I ever again find myself billed twice and double-booked by accident, I know just what to do: Throw a rager in one room, sleep until noon in the other. Maybe Muse can handle ordering the booze. A good party can never have too much. —Abram Brown ( [email protected] ) Weekend’s Latest Stories The Big Read Early Anthropic Investor Seeks VC Glory With Cash and Compute Anjney Midha, 34, wants to get chips in the hands of fledgling startups and academics while he bets on a moment in tech that he describes as the “revenge of the scientists.” Listening: “ Our Town ” The good folks of Gainesboro, Tenn. (population: around 900), know that plenty of outsiders look down their noses at their part of the world—figuring them to be a buncha “backwards, hateful racists,” as one longtime Gainesboro resident puts it. That impression of the place does certainly seem to be why Gainesboro found itself under siege a few years ago when a group of wealthy Christian nationalists started to buy up property in secret, hoping to transform the town into a hotbed for other Christian nationalists: They figured they’d encounter little pushback, especially considering how far their money could go in such an impoverished parish. (I could describe them as white supremacists, but the Christian nationalists do take great umbrage with that label.) “Our Town,” a fast-paced and confidently told podcast from Bloomberg and iHeartRadio, looks at how those Christian nationalists staged their attempted takeover of Gainesboro and how they accumulated their wealth and followers, the latter largely through podcasts. (Sigh—the internet.) It also documents the swift, spirited resistance Gainesboro mounted against them, which cheers the soul and asks us to revisit at least a few of the assumptions about America—and our fellow Americans—that may have become entrenched in our minds. —Abram Brown Reading: “ Profits, Prophets, Coaches, and Kings ” by Jared Diamond Many billions of dollars and quite a few Harvard Business School classes have gone toward trying to figure out what makes a great leader—and turning oafs into passable leaders. Now Jared Diamond, author of the Pulitzer-winning 1998 bestseller “Guns, Germs and Steel,” has set himself the task of defining the essential qualities behind leadership in his latest book, “Profits, Prophets, Coaches, and Kings.” He does so by examining famous figures in business, religion, sports and politics, marking what is surely not the only instance in which Elon Musk has been compared to Genghis Khan. In terms of capitalistic chieftains, Diamond finds that the most distinctive leaders are the ones who’ve had the benefit of both exquisite timing and ruthless execution: Jeff Bezos, for example, outmuscling his competitors in the internet’s Paleozoic Era. As far as politicians go, Diamond points out that the ones we truly remember most aren’t those that simply took a mandate from voters and carried it out, even though we so often say that’s exactly what we want our elected officials to do. Rather, they have championed their own bold ideas and convinced the plebs it was really all part of what they’d originally wanted. All of these conclusions are delivered with Diamond’s dry-humored wonkishness. I enjoyed one in particular: “Once one has decided that one is uniquely qualified to assume the burden of leadership,” Diamond writes, “one’s opinion of oneself is unlikely to change.” —A.B. Watching: “ The Gentlemen ” One of the great joys of “The Gentlemen,” the rollicking “Downton Abbey” meets “The Godfather” concoction from director Guy Ritchie, is to admire how nice Theo James looks as he struts around in magnificent tweed. James plays Eddie Horniman, the fictional Duke of Halstead, and as the Netflix series’ second season begins, it’s just as well that Eddie’s concentrating hard on restoring the family fortune to its fullest extent. The dry-cleaning bills must be enormous: His beautiful wool keeps getting splattered in blood. The first season of “The Gentlemen” found Eddie settling somewhat uncomfortably into his new role: paterfamilias of an old, down-on-its-luck aristocratic family that—much to his surprise—has tied its fortunes to an illicit marijuana operation. In this latest season, Eddie has found his footing and is thinking expansively, which puts him at odds with his business partners: a family of Cockney-accented gangsters—father Bobby (Ray Winstone), who operates from lightly monitored house arrest, and his two children, the very capable Susie (Kaya Scodelario) and strapping, bruised-knuckle Jack (Harry Goodwins). As Eddie’s ambitions get bigger, the stakes get higher and the mood grows darker. And since this is very much a Ritchie production, schemes and mayhem continue to fill the screen in manic fashion. One doltish accomplice gets fed to a tiger. Elsewhere, Eddie helms a chase through London after a motorcycle gang snatches away a $16 million Botticelli, which he needs to appease the Italian mafia. The comic relief comes from Hugh Bonneville’s mincing Lord Hawthorne, who needs bribing too—and lusts for the chance to pull down Jack’s plus fours. (To woo Jack, Lord Hawthorne wines and dines him; a plate of iced oysters is a treat of a “Spartacus” reference .) Whether Eddie can keep everyone in line and on board seems questionable based on Season 2’s flash-forward opening shot: his own bloodied body. Michael Corleone never had it so frantic. —A.B.

ReadSource

The Information AI / 3:07 PM

How to Use Google Gemini to Brainstorm Content and Thought Leadership

In almost any organization, there are practically infinite content ideas hiding in memos, emails, presentation decks, and meeting notes. But too often, this material mostly sits idle. Ideas that sparked excitement in the moment get ignored in favor of the countless little emergencies of office life, and eventually they are forgotten entirely. Even with many companies actively encouraging their executives to post organic thought leadership on professional networking sites, social media, and company channels, there are countless missed opportunities to turn off-the-cuff insights into valuable content. Google Gemini can help you transform your meeting notes into content plans, on-brand slogan ideas, and even booth mock-ups for your events team. Step 1: Brainstorm Blog Ideas Imagine you’ve just stepped out of a meeting with your social media team leads. You have a document full of notes (or maybe even a transcript) about what resonates with your audience, trending topics in your industry, target audience data, and keywords that are effective at driving engagement with your brand. Instead of letting this wealth of information stay locked inside your notes, open up a new Gemini chat and brainstorm how to turn your team’s sprawling strategy session into a series of relevant, focused blog posts. Here’s a prompt you can try: Generate a list of four relevant and engaging thought leadership blog post ideas for [company] based on trending topics, target audience analysis, and brand keywords. Gemini will return ideas for blog posts with catchy, clickable titles, along with information about target audience, keywords, angle, and tone. For example, if your meeting notes talk about how your customers are worried about their employees using unauthorized IT tools, Gemini might suggest a blog post titled “The Anatomy of a Zombie Subscription: How to Audit and Kill Shadow IT Instantly.” Step 2: Develop the Material The initial output will just be kernels of potential blog posts, but you can go back and forth with Gemini to develop them into real content. Take the “Zombie Subscription” blog, for example. You might ask Gemini to give you options for a “four tips” article structure, or to identify specific ideas from your meeting notes that merit extra attention. Use your own judgment—and your own language—during this process, rather than having Gemini write your entire blog post for you. Gemini can help with brainstorming, outlining, drafting, and revision, but your audience is ultimately reading the content to hear your own expert insights. Step 3: Distill the Main Message Maybe during your meeting, your team also discussed launching a new brand campaign. You know that your customers value your reliable and unique services, and your company has a long history of delivering for its customers. But sometimes, it can feel impossible to condense these big ideas into a few words that really communicate why customers come back to your company over and over. If you need help getting started with ideas for a new campaign tagline, open a new chat in Gemini and prompt something like: Generate three options for a new slogan emphasizing reliability, innovation, and a long history of popularity for [company]. For a B2B fintech company, Gemini might offer options like “Innovating Beyond the Bank. Trusted with Billions.” Or: “Built for Tomorrow. Proven by $10 Billion Today.” You may not hit the mark exactly on your first prompt. Maybe you want more (or less) alliteration, for example. Maybe you want it to be five words or fewer, unbroken by punctuation. You can keep asking Gemini for new variations until the new tagline is just right. Step 4: Mock Up Visuals for Events Often, one successful creative project sparks ideas for another. After you create your blog campaign and craft your new slogan, you might turn your attention to upcoming events. Try mocking up booth graphics ideas for your events team. Here’s a sample prompt: Create an image of a trade show booth using orange and blue colors. The booth should be modern and showcase interactive computer stations. Gemini will generate an image that you can sketch on in-app, giving you a quick, no-risk way to play with your potential trade show setup. And between the blog posts, the tagline, and the event visuals, you will have turned a couple of pages of messy meeting notes into the bones of an entire marketing campaign.

ReadSource

The Decoder / 10:27 AM

OpenAI's millennium proof dispute raises the question of whether researchers can trust AI labs

The fight over an AI-generated proof of a millennium problem is heating up. Mathematician Tristan Buckmaster accuses OpenAI of academic fraud, CEO Sam Altman rejects the allegations. Terence Tao warns that cases like this could "reverse centuries of tradition in open science." The article OpenAI's millennium proof dispute raises the question of whether researchers can trust AI labs appeared first on The Decoder .

ReadSource

Simon Willison LLMs / 11:55 PM

Some thoughts on the Navier–Stokes Millennium Prize Problem

On the Navier–Stokes Millennium Prize Problem introduces an impressive result from OpenAI, who used an unreleased model to produce a resolution to the Navier–Stokes existence and smoothness problem , one of the seven Millennium Prize Problems that have been subject to a $1,000,000 prize since May 24th, 2000. The discovery is somewhat overshadowed by accusations of skulduggery from Tristan Buckmaster, an NYU mathematics professor who was collaborating on related problems with Levent Alpöge, an accomplished mathematician who currently works for Anthropic. Tristan's complaint accompanied a hastily published version of their own results. Here's the PDF describing what happened . The very short version is that Tristan and Levent worked on the problem for almost a year, making extensive use of Claude and Codex (mainly GPT-5.6 Sol), then had a breakthrough on August 15th. The mathematical rumour mill kicked into gear and Tristan and Levent heard that OpenAI had heard that Anthropic had resolved "a major open problem", so they reached out and learned that OpenAI had a team working on a related problem, with a similar approach. Quoting Tristan: I asked when the first prompt had been sent by them. This question was not answered directly by OpenAI for some time. Eventually it was agreed that it had been sent in the past few days, after information about our work had reached OpenAI. I asked whether the model had been trained on, or had access to, our sessions in Codex, into which we had been putting all our drafts for the whole of this project. I was told the model did not look up user data. I asked again, about training, and I did not get an answer. It gets more complicated from there. The OpenAI team offered to wait for Tristan to publish, or to have him author a paper about their result, but were clear that Levent would not be invited as a co-author due to OpenAI's competitive relationship with his employer. Here's how OpenAI described their work: On Tuesday, September 1, we heard rumors that two Millennium Prize problems had been resolved. Inspired by these rumors and by the step change in performance of our internal model, we launched an effort to evaluate it on all open Millennium Prize problems and a few other high-impact problems. [...] The agents arrived at their resolution on Saturday, September 5, about 88 hours after the first agents were launched. Lean formalization and verification took an additional 17 hours via GPT‑6 Astra. Across all attempted problems, the agents sent 4.9 million messages and used about 300 billion output tokens. In the process of resolving the Navier–Stokes problem, the agents sent 2.7 million messages and used approximately 130 billion output tokens. (We don't know the cost structure of the internal model they used, but 300 billion output tokens at public API prices for GPT-6 Astra would cost $15,000,000 .) Here's where they provide their perspective on Tristan and Levent's work (emphasis mine): Our effort began on September 1st after hearing a rumor which we later realized was related to Levent Alpöge, an Anthropic employee, and Tristan Buckmaster, a math professor at NYU. After the completion of our full project and Lean verification (on September 6th), believing from the rumor they also had a solution of Navier–Stokes, we reached out to them to offer a concurrent release of our result and to recognize their priority in a joint announcement. [...] We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models . However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs unforced). My interpretation of what happened here is that OpenAI heard that some Millennium Prize problems had been solved using LLMs and saw this as an opportunity to demonstrate the power of their latest model, without thinking too hard about the optics of scooping a team who had been using OpenAI's own models to work on this problem for the best part of a year. This situation appears to mirror what's happening in the world of computer security right now. Anil Madhavapeddy recently pointed out that Just a rumour of a bug is enough to find a security exploit these days , because if someone knows that some software has an unpatched vulnerability, they can set their agents the task of finding it. Is the same now true of mathematics? Just knowing that there is an unpublished solution to a problem might trigger millions of dollars in LLM spending to get there first. This also highlights one of my ongoing frustrations about how all of this works. When an AI lab says that my data is "used to improve model performance", what does that actually mean ? My two favourite hypothetical questions regarding this used to be: If I'm running Codex and one of my API keys accidentally gets consumed in the context, what are the chances that someone else might ask for an API key in the future and get mine back? (I asked someone at OpenAI once and they called this the "regurgitation" problem and assured me that they take great pains to prevent that... but wouldn't describe how.) If I brainstorm with ChatGPT about potential new directions for my company, what's the chance that information might be exposed to a competitor in six months' time who asks "what might company X plan to do next"? My new preferred hypothetical for this is: If I use ChatGPT to help me partially solve a Millennium Prize problem, what are the chances that my work will influence training such that a later model helps someone else solve it first? Via Hacker News . Tags: mathematics , ai , openai , generative-ai , llms , training-data , ai-ethics

ReadSource

Bloomberg AI / 3:38 PM

How the AI ROI Gap Comes Down to Trust

Companies investing in trustworthy AI practices are far more likely to see strong returns from the technology, according to new research from SAS. But trust remains a hurdle: 97% of users override AI recommendations at least some of the time, while employees are less likely to trust increasingly autonomous AI agents. Jay Upchurch, SAS Executive Vice President and Chief Information Officer, joins Bloomberg's Anna Edwards on Bloomberg Television. (Source: Bloomberg)

ReadSource

VentureBeat AI / 12:01 PM

When agents act on their own, governance has to live in the data layer

Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it? These are your agents, running on your models, touching your data in your infrastructure — and the responsibility for what they do sits with you. That responsibility can’t be met in hindsight or with a set of abstract policies that live on paper but not in practice. Agents need rules in the context of the moment, because they don’t exercise overriding judgment of their own actions. Consider a simple rule: Never open the car door. Followed literally, an agent could never get in or out of the car at all. But if you change the context (the car has just crashed, there’s a fire, someone is hurt and needs to get out), then the rule you actually want is the opposite. Context in the moment is everything. We are asking agents to do intelligent things; that requires intelligent rules. The instinct is to add guardrails around the agent: instructions, policies, and monitoring layered above the model. Those mechanisms matter, but they share a structural limit: The car-door rule is plausible right up until the moment you actually have to decide whether to open the door. Controls at the agent layer are only as reliable as the agent’s output is predictable, and autonomy is precisely the property that makes that output hard to predict. Governance that depends on reviewing an action before it happens cannot keep pace with a system that acts in milliseconds, across many systems at once. Governance has to become executable , and enforced where agents actually do their work: at the operational data layer, in the context, and exactly at the moment it is happening. The data layer is the enforcement point Agents create value by touching data. They query it, retrieve it, transform it, and increasingly act on it. A policy that says an agent should not reach a certain class of data is meaningful only if the system can deny that access at the moment the agent requests it. Additionally, a principle that says AI must be auditable is meaningful only if the organization can reconstruct what the agent did, what data it touched, which user it acted for, and what resulted. When governance lives at the data layer, it holds regardless of how the agent was built or how it behaves, because the control is a property of the database itself, not a promise made by the agent. Agent behavior may be probabilistic. Governance cannot be The enterprise should not rely on a model choosing to follow policy. The policy has to be enforced by the system. That is the difference between hoping an actor stays in bounds and constructing bounds it cannot cross to begin with. The controls that make this real are ones many enterprises already run at the data layer: role- and attribute-based access, row- and column-level security, classification and masking, policy as code, and complete audit trails. What agents change is not the mechanism, but who the mechanism has to recognize. Identity management has to treat the agent as a principal in its own right, with its own identity and a purpose declared when the session opens. Once purpose is bound to identity, the policy engine can evaluate it the same way it evaluates role or department today, and the record of what happened can capture not just who acted and what they touched, but what they declared they were there to do. In practice, this resolves into nine controls, grouped under three imperatives: Enforce it Role- and attribute-based access control enforced at query time, for agents as well as users Dynamic column masking driven by the same policy path Agent identity as a first-class principal, with declared purpose bound at session start and the acting user preserved See it and prove it Classification and tagging that drives policy Session-level audit logging that records which agent acted, for which user, and under what declared purpose Lineage across pipelines, so a result can be traced back to the request that produced it Unify and harden Centralized, portable policy management Encryption at rest and in transit Consistent enforcement across on-prem, cloud, and sovereign or air-gapped environments “Declared purpose is what makes the difference. It becomes an attribute the access layer already understands, evaluated in the same policy path as role and row-level security. The enforcement mechanism does not change. What changes is that the agent's purpose is part of what it evaluates, and part of what the record proves afterward,” says Priyanka Jain, VP, product management, data & AI governance, EDB. Wherever you are in your AI adoption journey, enforcement at the data layer is what lets you move faster rather than slower. The controls are already in the database. The difference is that agents now have to pass through them. A digital leash, not a locked door The goal is not to stop agents from doing useful work. It is to define how far an agent can go, what it can touch, what it can change, what requires escalation, and how the organization can reconstruct events if something goes wrong. Governed this way, agents are identified, scoped, monitored, and auditable. The enterprise can adopt them faster , because security, risk, and leadership teams trust the operating model underneath. Open, sovereign, and enforceable at the source Built on open source Postgres , this open foundation keeps enterprises in control of where their data lives, who can reach it, and under what policy, without ceding governance to a layer they don’t own or can’t inspect. For regulated industries, that combination of data sovereignty and source-level enforcement isn’t a nice-to-have; it’s the precondition for putting agents into production at all. Agentic systems will keep getting more capable and more autonomous. That is a reason to be deliberate about where control lives, not a reason to slow down. The enterprises that enforce governance at the data layer can move aggressively on AI, because the thing protecting their data is more than just wishful thinking. EDB Postgres AI is an open, enterprise-grade sovereign data and AI platform that unifies transactional, analytical, and AI workloads — with governance enforced where the data lives. For the full framework, see EDB’s white paper Governing Agentic AI at Enterprise Speed . Max Romanenko is Chief Technology Officer at EDB. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected] .

ReadSource

VentureBeat AI / 2:30 PM

Orchestration is the new challenge for CX in the age of AI agents

Presented by Tata Communications Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it, says Gaurav Anand, global head of the Customer Interaction Suite at Tata Communications. "In the rush to deploy AI, organizations have largely bolted conversational AI onto legacy systems," Anand says. "As a result, while many enterprises have adopted digital tools, very few have platforms that are truly integrated, scaled, and capable of seamless orchestration." That gap creates a heavy cognitive load for human agents who must piece together context across disjointed tools to understand what an AI system has already told a customer. The challenge is not simply access to data, but the absence of a shared enterprise context that connects customer identities, interactions, transactions, policies, journeys, and operational systems into a common understanding. Traditional CX architecture was built for linear, human-driven routing, not for managing real-time data flows between autonomous AI systems, data lakes, and human workers. "Today's operational complexity is no longer about adding more intelligence," he adds. "It is about coordinating the existing intelligence across the enterprise, so the enterprise customer never feels the friction of those internal silos. That requires a shared context layer that allows AI systems, applications, and people to operate from the same understanding of the customer and the business." Why orchestration is replacing automation as the top CX priority As that coordination problem grows, Anand says the strategic priority inside enterprises is shifting from automation to orchestration. "Automation solves individual tasks, whereas orchestration connects them into end-to-end outcomes," Anand says. "The next evolution is context-aware orchestration, where AI agents, applications, and human workers operate using a shared understanding of customers, processes, and business intent rather than isolated system records." As organizations accumulate more bots, agents, and AI tools, managing them grows exponentially more complex. Anand says the competitive advantage now sits less in deploying automation and more in how intelligently systems hand off work, collaborate, and escalate. The trap of bolting AI onto legacy systems Companies that simply place a voice AI agent in front of an existing system are repeating the same old mistake. Instead of improving the experience, they end up recreating the deterministic phone menus AI was supposed to replace. The real benefit of AI is the scale, speed, and orchestration it provides. Anand points to a wave of consolidation across the industry, as established contact center providers acquire AI-native firms to close capability gaps and strengthen their customer experience offerings. The broader industry shift reflects a growing recognition that enterprises need more than channels and automation; they need an intelligence layer capable of orchestrating AI, people, data, and workflows across the business. The goal across industries is to make AI the connective layer between customers, employees, and enterprise systems. To achieve that, organizations increasingly need a common enterprise ontology: a shared business vocabulary that aligns customer data, products, policies, SOPs, transactions, and workflows across otherwise disconnected platforms. Tata Communications’ solution is the Interaction Fabric, an orchestration layer that unifies contact center, messaging, collaboration, AI, and customer data while coordinating AI agents, channels, and enterprise systems in real time. Underpinning that orchestration is a context-driven architecture that continuously connects identities, conversations, transactions, and operational data so interactions retain continuity across channels and touchpoints. That means AI and agents can move across voice, WhatsApp, chat, email, and CRM workflows without losing customer context. Identity, intent, and AI-driven insight flow continuously across channels instead of remaining trapped in disconnected applications. The next phase of orchestration is not simply coordinating tasks across systems, but coordinating them through a shared understanding of the enterprise. Context graphs, built on enterprise ontologies, create that common understanding by connecting customers, interactions, products, policies, decisions, and outcomes across organizational silos. This allows AI agents and human workers to operate from the same source of context, driving more accurate decisions, seamless handoffs, and consistent customer experiences. But synchronizing customer intent, conversation history, enterprise data, and AI decision-making across channels only works without lag. Legacy networks not designed for modern data frequency create what Anand calls data gravity, producing latency and inconsistent journeys as users switch channels. "The underlying network needs to be engineered to be as agile as the AI systems running on top of it," he explains. "Interactions stay synchronous and technology itself becomes invisible, leaving only an experience that feels effortless." Making AI a better partner for human agents Effective shared visibility between human agents and AI systems starts with the agent experience rather than any single technology. The most effective implementations allow both the AI and human agent to operate from the same contextual understanding of the customer, ensuring that information gathered in one interaction can inform the next regardless of channel or system. Automated call summaries, real-time sentiment analysis, and AI-powered assistance provide agents with instant, actionable insights and suggested next steps directly within their workflow. That allows AI to handle routine, high-volume tasks such as password resets, delivery tracking, and account updates, while human agents focus on interactions requiring judgment and empathy. "If a customer is facing a sudden crisis like a fraudulent transaction, the AI can instantly block the card, but it cannot provide the emotional comfort and delicate communication needed in that moment of panic," Anand says. "The answer to the dilemma is intelligent orchestration, rather than a choice between systems." In practice, AI handles the immediate technical transaction, while real-time sentiment analysis recognizes the customer's distress and routes the call to a human expert. The objective is to orchestrate AI and human agents together so efficiency never comes at the cost of brand trust and loyalty. Building a unified CX architecture Moving from fragmented experimentation to coordinated orchestration requires both technical and organizational change, Anand says, beginning with consolidating data and fragmented point solutions onto a unified, cloud-first platform. "IT and CX teams need to work more collaboratively," he explains, describing that alignment as the second necessary shift, this time at the organizational level. At the architecture level, Anand says communication APIs need to be embedded into the enterprise's core so every function operates from the same customer context instead of maintaining its own siloed data. Increasingly, this means moving beyond integration alone toward a contextual architecture where a shared ontology and context graph provide a common understanding across CX, operations, sales, service, and AI systems. The deeper organizational change, he says, is a mindset shift from reactive support toward proactive, predictive, and personalized engagement, which he calls the three Ps. How AI agents will shape the future of CX Customer engagement over the next several years will be defined by real-time intelligence, increasing autonomy, and seamless orchestration across touchpoints, and persistent enterprise context that follows customers, employees, and AI agents wherever interactions occur. Rather than analyzing interactions after the fact, enterprises will increasingly shape conversations in real time. "The future of CX will be defined by simplification, aligning data, infrastructure, and operating models around clear customer outcomes rather than adding more models and tools," Anand says. "The rise of AI-powered agents and agent-to-agent interactions is a defining trend, with AI systems moving beyond assisting humans to independently managing and resolving interactions, creating a largely invisible layer of engagement that improves speed and efficiency." Human agents will increasingly work alongside AI, supported by real-time conversational intelligence and next-best-action recommendations to deliver what Anand calls Total Experience: a unified model that brings together customer, employee, and AI-driven experiences. Tata Communications is building toward that future through its Voice AI, AI Workers, and Total Experience Hub solutions. "Ultimately, customer engagement will evolve from being reactive to predictive and increasingly generative," Anand says. "Enterprises won't just be responding to needs, but actively shaping and improving customer journeys in real time." Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected] .

ReadSource

Bloomberg AI / 4:50 PM

Big Tech Is Incapable of Solving Its Own Problem, Says Mukunda

Bill Gates, the billionaire co-founder of Microsoft Corp., published a 6,000-word essay on Aug. 26 saying that he would slow the development of artificial intelligence if he could. He elaborated in an interview with the New York Times, saying “in private, people who understand how good this stuff is, and how much better it’s getting, they’re very worried” but few are raising the issues in public. The public is already convinced the tech industry can’t be trusted with its own creations. Some legal news last week explains why. A bipartisan coalition of 51 attorneys general announced a proposed settlement with Meta Platforms Inc. that obligates the social media company that runs Facebook and Instagram to put tight restrictions on how children use its platforms. Gautam Mukunda, Lecturer at the Yale School of Management and Bloomberg Opinion contributor, joins Bloomberg Intelligence to discuss. (Source: Bloomberg)

ReadSource

Latest story in this edition: 1:48 AM

Back to front page