<a href="https://lightfield.app/">Lightfield</a>, a customer relationship management platform built entirely around artificial intelligence, officially launched to the public this week after a year of quiet development — a bold pivot by a startup that once had <a href="https://www.forbes.com/sites/rashishrivastava/2024/04/23/the-prompt-the-latest-ai-startup-to-face-reality/">20 million users</a> and <a href="https://www.forbes.com/sites/alexkonrad/2023/02/22/storytelling-ai-startup-tome-raises-43-million/">$43 million in the bank</a> building something completely different.The San Francisco-based company is positioning itself as a fundamental reimagining of how businesses track and manage customer relationships, abandoning the manual data entry that has defined CRMs for decades in favor of a system that automatically captures, organizes, and acts on customer interactions. With more than 100 early customers already using the platform daily — over half spending more than an hour per day in the system — <a href="https://lightfield.app/">Lightfield</a> is a direct challenge to the legacy business models of <a href="https://www.salesforce.com/">Salesforce</a> and <a href="https://www.hubspot.com/">HubSpot</a>, both of which generate billions in annual revenue.&quot;The CRM, categorically, is perhaps the most complex and lowest satisfaction piece of software on Earth,&quot; said Keith Peiris, Lightfield&#x27;s co-founder and CEO, in an exclusive interview with VentureBeat. &quot;CRM companies have tens of millions of users, and you&#x27;d be hard-pressed to find a single one who actually loves the product. That problem is our opportunity.&quot;The <a href="https://lightfield.app/">general availability</a> announcement marks an unusual inflection point in enterprise software: a company betting that large language models have advanced enough to replace structured databases as the foundation of business-critical systems. It&#x27;s a wager that has attracted backing from <a href="https://www.coatue.com/">Coatue Management</a>, which led the company&#x27;s Series A when it was still building presentation software under the name Tome.<h2>How Tome&#x27;s founders abandoned 20 million users to build a CRM from scratch</h2>The story behind Lightfield&#x27;s creation reflects both conviction and pragmatism. <a href="https://www.fastcompany.com/90827339/presentation-tool-tome-launches-ai-to-help-make-storytelling-simpler">Tome</a> had achieved significant viral success as an AI-powered presentation platform, gaining millions of users who appreciated its visual design and ease of use. But Peiris said the team concluded that building lasting differentiation in the general-purpose presentation market would prove difficult, even with a working product and real user traction.&quot;Tome went viral as an AI slides product, and it was visually delightful and easy to use—the first real generative AI-based presentation platform,&quot; Peiris explained. &quot;But, the more people used it, the more I realized that to really help people communicate something—anything—we needed more context.&quot;That realization led to a fundamental rethinking. The team observed that the most effective communication requires deep understanding of relationships, company dynamics, and ongoing conversations — context that exists most richly in sales and customer-facing roles. Rather than building a horizontal tool for everyone, they decided to build vertically for go-to-market teams.&quot;We chose this lane, &#x27;sales,&#x27; because so many people in these roles used Tome, and it seemed like the most logical place to go vertical,&quot; Peiris said. The team reduced headcount to a core group of engineers and spent a year building in stealth.<a href="https://signal.nfx.com/investors/dan-rose">Dan Rose</a>, a senior advisor at <a href="https://www.coatue.com/">Coatue</a> who led the original investment in Tome, said the pivot validated his conviction in the founding team. &quot;It takes real guts to pivot, and even more so when the original product is working,&quot; Rose said. &quot;They shrunk the team down to a core group of engineers and got to work building Lightfield. This was not an easy product to build, it is extremely complex under the hood.&quot;<h2>Why Lightfield stores complete conversations instead of forcing data into fields</h2>What distinguishes <a href="https://lightfield.app/">Lightfield</a> from traditional CRMs is architectural, not cosmetic. While <a href="https://www.salesforce.com/">Salesforce</a>, <a href="https://www.hubspot.com/">HubSpot</a>, and their competitors require users to define rigid data schemas upfront — dropdown menus, custom fields, checkbox categories — and then manually populate those fields after every interaction, Lightfield stores the complete, unstructured record of what customers actually say and do.&quot;Traditional CRMs force every interaction through predefined fields — they&#x27;re compressing rich, nuanced customer conversations into structured database entries,&quot; Peiris said. &quot;We store customer data in its raw, lossless form. That means we&#x27;re capturing significantly more detail and context than a traditional CRM ever could.&quot;In practice, this means the system automatically records and transcribes sales calls, ingests emails, monitors product usage, and maintains what the company calls a &quot;relationship timeline&quot; — a complete chronological record of every touchpoint between a company and its customers. AI models then extract structured information from this raw data on demand, allowing companies to reorganize their data model without manual rework.&quot;If you realize you need different fields or want to reorganize your schema entirely, the system can remap and refill itself automatically,&quot; Peiris explained. &quot;You&#x27;re not locked into decisions you made on day one when you barely understood your sales process.&quot;The system also generates meeting preparation briefs, drafts follow-up emails based on conversation context, and can be queried in natural language — capabilities that represent a departure from the passive database model that has defined CRMs since the category&#x27;s inception in the 1980s.<h2>Sales teams report reviving dead deals and cutting response times from months to days</h2>Customer testimonials suggest the automation delivers measurable impact, particularly for small teams without dedicated sales operations staff. Tyler Postle, co-founder of <a href="http://voker.ai">Voker.ai</a>, said Lightfield&#x27;s AI agent helped him revive more than 40 stalled opportunities in a single two-hour session — leads he had neglected for six months while using HubSpot.&quot;Within 2 days, 10 of those were revived and became active opps that moved to poc,&quot; Postle said. &quot;The problem was, instead of being a tool of action and autotracking—HubSpot was a tool where I had to do the work to record customer convos. Using HubSpot I was a data hygienist. Using Lighfield, I’m a closer.&quot;Postle reported that his response times to prospects improved from weeks or months to one or two days, a change noticeable enough that customers commented on it. &quot;Our prospects and customers have even noticed it,&quot; he said.Radu Spineanu, co-founder of <a href="https://humbleops.ai/">Humble Ops</a>, highlighted a specific feature that addresses what he views as the primary cause of lost deals: simple neglect. &quot;The killer feature is asking &#x27;who haven&#x27;t I followed up with?&#x27;&quot; Spineanu said. &quot;Most deals die from neglect, not rejection. Lightfield catches these dropped threads and can draft and send the follow-up immediately. That&#x27;s prevented at least three deals from going cold this quarter.&quot;Spineanu had evaluated competing modern CRMs including <a href="https://attio.com/">Attio</a> and <a href="https://www.clay.com/">Clay</a> before selecting <a href="https://lightfield.app/">Lightfield</a>, dismissing <a href="https://www.salesforce.com/">Salesforce</a> and <a href="https://www.hubspot.com/">HubSpot</a> as &quot;built for a different era.&quot; He said those platforms assume companies have dedicated operations teams to configure workflows and maintain data quality — resources most early-stage companies lack.<h2>Why Y Combinator startups are rejecting Salesforce and starting with AI-native tools</h2>Peiris claims that the current batch of <a href="https://www.ycombinator.com/">Y Combinator</a> startups — widely viewed as a bellwether for early-stage company behavior — have largely rejected both <a href="https://www.salesforce.com/">Salesforce</a> and <a href="https://www.hubspot.com/">HubSpot</a>. &quot;If you were to poll a random sampling of current YC startups and ask whether they&#x27;re using Salesforce or HubSpot, the overwhelming answer would be &#x27;no,&#x27;&quot; he said. &quot;Salesforce is too expensive, too complex to set up, and frankly doesn&#x27;t do enough to justify the investment for an early-stage company.&quot;According to Peiris, most startups begin with spreadsheets and eventually graduate to a first CRM — a transition point where Lightfield aims to intercede. &quot;Increasingly, they&#x27;re choosing Lightfield instead and skipping that intermediate step entirely,&quot; he said.This represents a familiar pattern in enterprise software disruption: a new generation of companies forming habits around different tools, creating an opening for challengers to establish themselves before businesses grow large enough to face pressure toward industry-standard platforms. The company&#x27;s strategy appears to deliberately target this window, aiming to grow alongside early customers and become embedded in their processes as they scale.<h2>Can Salesforce and HubSpot retrofit their legacy systems for AI, or is the architecture too old?</h2>Both <a href="https://www.salesforce.com/">Salesforce</a> and <a href="https://www.hubspot.com/">HubSpot</a> have announced AI features in recent quarters, adding capabilities like conversation intelligence and automated data entry to their existing platforms. The question facing Lightfield is whether established vendors can incorporate similar capabilities—leveraging their existing customer bases and integrations — or whether fundamental architectural differences create a genuine moat.Peiris argues the latter. &quot;The fundamental difference is in how we store data,&quot; he said. &quot;Because we have access to that complete context, the analysis we provide and the work we generate tends to be substantially higher quality than tools built on top of traditional database structures.&quot;Existing conversation intelligence tools like <a href="https://www.gong.io/">Gong</a> and <a href="http://revenue.io">Revenue.io</a>, which analyze sales calls and provide coaching insights, already serve similar functions but require Salesforce instances to operate. Peiris said Lightfield&#x27;s advantage comes from unifying the entire data model rather than layering analysis on top of fragmented systems.&quot;We have a more complete picture of each customer because we integrate company knowledge, communication sync, product analytics, and full CRM detail all in one place,&quot; he said. &quot;That unified context means the work being generated in Lightfield—whether it&#x27;s analysis, follow-ups, or insights—tends to be significantly higher quality.&quot;<h2>The privacy and accuracy concerns that come with AI-automated customer interactions</h2>The architecture creates obvious risks. Storing complete conversation histories raises privacy concerns, and relying on large language models to extract and interpret information introduces the possibility of errors—what AI researchers call hallucinations.Peiris acknowledged both issues directly. On privacy, the company maintains that call recording follows standard practices, with visible notifications that recording is in progress, and that storing sales correspondence mirrors what CRM vendors have done for decades. The company has achieved SOC 2 Type I certification and is pursuing both SOC 2 Type II and HIPAA compliance. &quot;We don&#x27;t train models on customer data, period,&quot; Peiris said.On accuracy, he was similarly forthright. &quot;Of course it happens,&quot; Peiris said when asked about misinterpretations. &quot;It&#x27;s impossible to completely eliminate hallucinations when working with large language models.&quot;The company&#x27;s approach is to require human approval before sending customer communications or updating critical fields — positioning the system as augmentation rather than full automation. &quot;We&#x27;re building a tool that amplifies human judgment, not one that pretends to replace it entirely,&quot; Peiris said.This is a more cautious stance than some AI-native software companies have taken, reflecting both technical realism about current model capabilities and potential liability concerns around customer-facing mistakes.<h2>How Lightfield plans to consolidate ten different sales tools into one platform</h2>Lightfield&#x27;s pricing strategy reflects a broader thesis about enterprise software economics. Rather than charging per-seat fees for a point solution, the company is positioning itself as a consolidated platform that can replace multiple specialized tools — sales engagement platforms, conversation intelligence systems, meeting assistants, and the CRM itself.&quot;The real problem is that running a modern go-to-market function requires cobbling together 10 different independent point solutions,&quot; Peiris said. &quot;When you pay for 10 separate seat licenses, you&#x27;re essentially paying 10 different companies to solve the same foundational problems over and over again.&quot;The company operates primarily through self-service signup rather than enterprise sales teams, which Peiris argues allows for lower pricing while maintaining margins. This is a common playbook among modern SaaS companies but represents a fundamental difference from Salesforce&#x27;s model, which relies heavily on direct sales and customer success teams.Whether this approach can support a sustainable business at scale remains unproven. The company&#x27;s current customer base skews heavily toward early-stage startups—more than 100 <a href="https://www.ycombinator.com/">Y Combinator</a> companies, according to the company — a segment with limited budgets and high failure rates.But Lightfield is betting it can become the system of record for a cohort of fast-growing companies, eventually creating an installed base comparable to how Salesforce established itself decades ago. The company&#x27;s trajectory will likely depend on whether AI capabilities alone provide sufficient differentiation—or whether incumbents can adapt quickly enough to defend their positions.<h2>The real test: whether sales teams will trust AI enough to let it run their business</h2>The company has outlined several areas for expansion, including an open platform for workflows and webhooks that would allow third-party integrations. Early customers have specifically requested connections with tools like <a href="https://www.apollo.io/">Apollo</a> for prospecting and <a href="https://slack.com/">Slack</a> for team communication — gaps that Postle, the <a href="http://voker.ai">Voker.ai</a> founder, acknowledged but dismissed as temporary.&quot;The fact that HS and Salesforce have these integrations already isn&#x27;t a moat,&quot; Postle said. &quot;HS and Salesforce are going to lose to lightfield because they aren&#x27;t AI native, no matter how much they try to pretend to be.&quot;Rose highlighted an unusual use case that emerged during Lightfield&#x27;s own development: the company&#x27;s product team used the CRM itself to analyze customer conversations and identify feature requests. &quot;In this sense, Lightfield more than just a sales database, it&#x27;s a customer intelligence layer,&quot; Rose said.This suggests potential applications beyond traditional sales workflows, positioning the system as infrastructure for any function that requires understanding customer needs—product development, customer success, even marketing strategy.For now, the company is focused on proving the core value proposition with early-stage companies. But the broader question Lightfield raises extends beyond CRM software specifically: whether AI capabilities have advanced sufficiently to replace structured databases as the foundation of enterprise systems, or whether the current generation of large language models remains too unreliable for business-critical functions.The answer will likely emerge not from technical benchmarks but from customer behavior—whether sales teams actually trust AI-generated insights enough to base decisions on them, and whether the efficiency gains justify the inherent unpredictability of working with systems that approximate rather than calculate.<a href="https://lightfield.app/">Lightfield</a> is betting that the trade-off has already shifted in favor of approximation, at least for the millions of salespeople who currently view their CRM as an obstacle rather than an asset. Whether that bet proves correct will help define the next generation of enterprise software.

تم إطلاق Lightfield، وهي منصة لإدارة علاقات العملاء تعتمد على الذكاء الاصطناعي، رسميًا بعد عام من التطوير. تم إنشاء المنصة من قبل مؤسسي Tome، الذين قاموا سابقًا ببناء تطبيق تقديمي شهير مع 20 مليون مستخدم، وتهدف المنصة إلى تحويل كيفية إدارة الشركات للتفاعلات مع العملاء من خلال أتمتة جمع البيانات وتنظيمها. مع وجود أكثر من 100 عميل مبكر يستخدمون المنصة بالفعل، تسعى Lightfield إلى إعادة تعريف ممارسات إدارة علاقات العملاء التي اعتمدت لعقود على إدخال البيانات اليدوي.

Lightfield, una plataforma de gestión de relaciones con clientes nativa de IA, se lanzó oficialmente después de un año de desarrollo. Creada por los fundadores de Tome, que anteriormente construyeron una aplicación de presentación viral con 20 millones de usuarios, la plataforma busca transformar la gestión de interacciones con clientes mediante la automatización de la captura y organización de datos. Con más de 100 primeros usuarios ya utilizando la plataforma, Lightfield pretende redefinir las prácticas de CRM que han dependido de la entrada manual de datos durante décadas.

Lightfield, une plateforme de gestion de la relation client native de l'IA, a été officiellement lancée après un an de développement. Créée par les fondateurs de Tome, qui avaient auparavant développé une application de présentation virale avec 20 millions d'utilisateurs, la plateforme vise à transformer la gestion des interactions clients en automatisant la capture et l'organisation des données. Avec plus de 100 premiers utilisateurs déjà actifs, Lightfield cherche à redéfinir les pratiques CRM qui reposent depuis longtemps sur la saisie manuelle des données.

Lightfield, an AI-native customer relationship management platform, has officially launched after a year of development. The platform, created by the founders of Tome, who previously built a viral presentation app with 20 million users, aims to transform how businesses manage customer interactions by automating data capture and organization. With over 100 early adopters already utilizing the platform, Lightfield seeks to redefine CRM practices that have long relied on manual data entry.

Tome's founders ditch viral presentation app with 20M users to build AI-native CRM Lightfield

New attacks show the effects of the Salesloft breach are still being felt.

أفادت شركة Salesforce بأن بيانات العملاء قد تكون تعرضت للاختراق بسبب حادث يتعلق بشركة Gainsight. الشركة تحقق حاليًا في تقارير عن 'نشاط غير عادي' مرتبط بالاختراق. يسلط هذا الحادث الضوء على التداعيات المستمرة للاختراق السابق لشركة Salesloft، مما يشير إلى أن آثار مثل هذه الحوادث الأمنية يمكن أن تستمر مع مرور الوقت.

Salesforce ha informado que los datos de los clientes pueden haber sido expuestos debido a un incidente relacionado con Gainsight. La compañía está investigando informes de 'actividad inusual' vinculada a la violación. Este incidente resalta las repercusiones continuas de la violación anterior de Salesloft, indicando que los efectos de tales incidentes de seguridad pueden persistir con el tiempo.

Salesforce a signalé que des données clients pourraient avoir été exposées en raison d'un incident impliquant Gainsight. L'entreprise enquête actuellement sur des rapports d'« activité inhabituelle » liée à cette violation. Cet incident met en lumière les répercussions continues de la violation antérieure de Salesloft, indiquant que les effets de tels incidents de sécurité peuvent persister dans le temps.

Salesforce has reported that customer data may have been exposed due to an incident involving Gainsight. The company is currently investigating reports of 'unusual activity' linked to the breach. This incident highlights the ongoing repercussions of the earlier Salesloft breach, indicating that the effects of such security incidents can persist over time.

Salesforce says customer data may be exposed in Gainsight incident - "unusual activity" being probed

<a href="https://www.salesforce.com/">Salesforce</a> launched a suite of monitoring tools on Thursday designed to solve what has become one of the thorniest problems in corporate artificial intelligence: Once companies deploy AI agents to handle real customer interactions, they often have no idea how those agents are making decisions.The new capabilities, built into <a href="https://www.salesforce.com/platform/">Salesforce&#x27;s Agentforce 360 Platform</a>, give organizations granular visibility into every action their AI agents take, every reasoning step they follow, and every guardrail they trigger. The move comes as businesses grapple with a fundamental tension in AI adoption — the technology promises massive efficiency gains, but executives remain wary of autonomous systems they can&#x27;t fully understand or control.&quot;You can&#x27;t scale what you can&#x27;t see,&quot; said Adam Evans, executive vice president and general manager of Salesforce AI, in a statement announcing the release. The company says businesses have increased AI implementation by 282% recently, creating an urgent need for monitoring systems that can track fleets of AI agents making real-world business decisions.The challenge Salesforce aims to address is deceptively simple: AI agents work, but no one knows why. A customer service bot might successfully resolve a tax question or schedule an appointment, but the business deploying it can&#x27;t trace the reasoning path that led to that outcome. When something goes wrong — or when the agent encounters an edge case — companies lack the diagnostic tools to understand what happened.&quot;Agentforce Observability acts as a mission control system to not just monitor, but also analyze and optimize agent performance,&quot; said Gary Lerhaupt, vice president of Salesforce AI who leads the company&#x27;s observability work, in an exclusive interview with VentureBeat. He emphasized that the system delivers business-specific metrics that traditional monitoring tools miss. &quot;In service, this could be engagement or deflection rate. In sales, it could be leads assigned, converted, or reply rates.&quot;<h2>How AI monitoring tools helped 1-800Accountant and Reddit track autonomous agent decision-making</h2>The stakes become clear in early customer deployments. Ryan Teeples, chief technology officer at <a href="https://1800accountant.com/">1-800Accountant</a>, said his company deployed Agentforce agents to serve as a 24/7 digital workforce handling complex tax inquiries and appointment scheduling. The AI draws on integrated data from audit logs, customer support history, and sources like IRS publications to provide instant responses — without human intervention.For a financial services firm handling sensitive tax information during peak season, the inability to see how the AI was making decisions would be a dealbreaker. &quot;With this level of sensitive information and the fast pace in which we move during tax season in particular, Observability allows us to have full trust and transparency with every agent interaction in one unified view,&quot; Teeples said.The observability tools revealed insights Teeples didn&#x27;t expect. &quot;The optimization feature has been the most eye opening for us — giving full observability into agent reasoning, identifying performance gaps and revealing how our agents are making decisions,&quot; he said. &quot;This has helped us quickly diagnose issues that would&#x27;ve otherwise gone undetected and configure guardrails in response.&quot;The business impact proved substantial. <a href="https://www.salesforce.com/agentforce/">Agentforce</a> resolved over 1,000 client engagements in the first 24 hours at 1-800Accountant. The company now projects it can support 40% client growth this year without recruiting and training seasonal staff, while freeing up 50% more time for CPAs to focus on complex advisory work rather than administrative tasks.<a href="https://www.reddit.com/">Reddit</a> has seen similar results since deploying the technology. John Thompson, vice president of sales strategy and operations at the social media platform, said the company has deflected 46% of support cases since launching Agentforce for advertiser support. &quot;By observing every Agentforce interaction, we can understand exactly how our AI navigates advertisers through even the most complex tools,&quot; Thompson said. &quot;This insight helps us understand not just whether issues are resolved, but how decisions are made along the way.&quot;<h2>Inside Salesforce&#x27;s session tracing technology: Logging every AI agent interaction and reasoning step</h2>Salesforce built the observability system on two foundational components. The <a href="https://help.salesforce.com/s/articleView?id=ai.generative_ai_session_trace_setup.htm&amp;type=5">Session Tracing Data Model</a> logs every interaction — user inputs, agent responses, reasoning steps, language model calls, and guardrail checks — and stores them securely in Data 360, Salesforce&#x27;s data platform. This creates what the company calls &quot;unified visibility&quot; into agent behavior at the session level.The second component, <a href="https://www.mulesoft.com/ai/agent-fabric">MuleSoft Agent Fabric</a>, addresses a problem that will become more acute as companies build more AI systems: agent sprawl. The tool provides what Lerhaupt describes as &quot;a single pane of glass across every agent,&quot; including those built outside the Salesforce ecosystem. Agent Fabric&#x27;s Agent Visualizer creates a visual map of a company&#x27;s entire agent network, giving visibility across all agent interactions from a single dashboard.The observability tools break down into three functional areas. Agent Analytics tracks performance metrics, surfaces KPI trends over time, and highlights ineffective topics or actions. Agent Optimization provides end-to-end visibility of every interaction, groups similar requests to uncover patterns, and identifies configuration issues. Agent Health Monitoring, which will become generally available in Spring 2026, tracks key health metrics in near real-time and sends alerts on critical errors and latency spikes.Pierre Matuchet, senior vice president of IT and digital transformation at <a href="https://www.adecco.com/">Adecco</a>, said the visibility helped his team build confidence even before full deployment. &quot;Even during early notebook testing, we saw the agent handle unexpected scenarios, like when candidates didn&#x27;t want to answer questions already covered in their CVs, appropriately and as designed,&quot; Matuchet said. &quot;Agentforce Observability helped us identify unanticipated user behavior and gave us confidence, even before the agent went live, that it could act responsibly and reliably.&quot;<h2>Why Salesforce says its AI observability tools beat Microsoft, Google, and AWS monitoring</h2>The announcement puts Salesforce in direct competition with <a href="https://microsoft.com/">Microsoft</a>, <a href="https://www.google.com/">Google</a>, and <a href="https://aws.amazon.com/">Amazon Web Services</a>, all of which offer monitoring capabilities built into their AI agent platforms. Lerhaupt argued that enterprises need more than the basic monitoring those providers offer.&quot;Observability comes out-of-the-box standard with Agentforce at no extra cost,&quot; Lerhaupt said, positioning the offering as comprehensive rather than supplementary. He emphasized that the tools provide &quot;deeper insight than ever before&quot; by capturing &quot;the full telemetry and reasoning behind every agentic interaction&quot; through the Session Tracing Data Model, then using that data to &quot;provide key analysis and session quality scoring to help customers optimize and improve their agents.&quot;The competitive positioning matters because enterprises face a choice: build their AI infrastructure on a cloud provider&#x27;s platform and use its native monitoring tools, or adopt a specialized observability layer like Salesforce&#x27;s. Lerhaupt framed the decision as one of depth versus breadth. &quot;Enterprises need more than basic monitoring to measure the success of their AI deployments,&quot; he said. &quot;They need full visibility into every agent interaction and decision.&quot;<h2>The 1.2 billion workflow question: Are AI agent deployments moving from pilot projects to production?</h2>The broader question is whether <a href="https://www.salesforce.com/">Salesforce</a> is solving a problem most enterprises will face imminently or building for a future that remains years away. The company&#x27;s 282% surge in AI implementation sounds dramatic, but that figure doesn&#x27;t distinguish between production deployments and pilot projects.When asked about this directly, Lerhaupt pointed to customer examples rather than offering a breakdown. He described a three-phase journey from experimentation to scale. &quot;On Day 0, trust is the foundation,&quot; he said, citing 1-800Accountant&#x27;s 70% autonomous resolution of chat engagements. &quot;Day 1 is where designing ideas to become real, usable AI,&quot; with Williams Sonoma delivering more than 150,000 AI experiences monthly. &quot;On Day 2, once trust and design are built, it becomes about scaling early wins into enterprise-wide outcomes,&quot; pointing to Falabella&#x27;s 600,000 AI workflows per month that have grown fourfold in three months.Lerhaupt said Salesforce has 12,000-plus customers across 39 countries running Agentforce, powering 1.2 billion agentic workflows. Those numbers suggest the shift from pilot to production is already underway at scale, though the company didn&#x27;t provide a breakdown of how many customers are running production workloads versus experimental deployments.The economics of AI deployment may accelerate adoption regardless of readiness. Companies face mounting pressure to reduce headcount costs while maintaining or improving service levels. AI agents promise to resolve that tension, but only if businesses can trust them to work reliably. Observability tools like Salesforce&#x27;s represent the trust layer that makes scaled deployment possible.<h2>What happens after AI agent deployment: Why continuous monitoring matters more than initial testing</h2>The deeper story is about a shift in how enterprises think about AI deployment. The official announcement framed this clearly: &quot;The agent development lifecycle begins with three foundational steps: build, test, and deploy. While many organizations have already moved past the initial hurdle of creating their first agents, the real enterprise challenge starts immediately after deployment.&quot;That framing reflects a maturing understanding of AI in production environments. Early AI deployments often treated the technology as a one-time implementation — build it, test it, ship it. But AI agents behave differently than traditional software. They learn, adapt, and make decisions based on probabilistic models rather than deterministic code. That means their behavior can drift over time, or they can develop unexpected failure modes that only emerge under real-world conditions.&quot;Building an agent is just the beginning,&quot; Lerhaupt said. &quot;Once the trust is built for agents to begin handling real work, companies may start by seeing the results, but may not understand the &#x27;why&#x27; behind them or see areas to optimize. Customers interact with products—including agents—in unexpected ways and to optimize the customer experience, transparency around agent behavior and outcomes is critical.&quot;Teeples made the same point more bluntly when asked what would be different without observability tools. &quot;This level of visibility has given full trust in continuing to expand our agent deployment,&quot; he said. The implication is clear: without visibility, deployment would slow or stop. 1-800Accountant plans to expand Slack integrations for internal workflows, deploy Service Cloud Voice for case deflection, and leverage Tableau for conversational analytics—all dependent on the confidence that observability provides.<h2>How enterprise AI trust issues became the biggest barrier to scaling autonomous agents</h2>The recurring theme in customer interviews is trust, or rather, the lack of it. AI agents work, sometimes spectacularly well, but executives don&#x27;t trust them enough to deploy them widely. Observability tools aim to convert black-box systems into transparent ones, replacing faith with evidence.This matters because trust is the bottleneck constraining AI adoption, not technological capability. The models are powerful enough, the infrastructure is mature enough, and the business case is compelling enough. What&#x27;s missing is executive confidence that AI agents will behave predictably and that problems can be diagnosed and fixed quickly when they arise.Salesforce is betting that observability tools can remove that bottleneck. The company positions <a href="https://www.salesforce.com/agentforce/observability/">Agentforce Observability</a> not as a monitoring tool but as a management layer—&quot;just like managers work with their human employees to ensure they are working towards the right objectives and optimizing performance,&quot; Lerhaupt said.The analogy is telling. If AI agents are becoming digital employees, they need the same kind of ongoing supervision, feedback, and optimization that human employees receive. The difference is that AI agents can be monitored with far more granularity than any human worker. Every decision, every reasoning step, every data point consulted can be logged, analyzed, and scored.That creates both opportunity and obligation. The opportunity is continuous improvement at a pace impossible with human workers. The obligation is to actually use that data to optimize agent performance, not just collect it. Whether enterprises can build the organizational processes to turn observability data into systematic improvement remains an open question.But one thing has become increasingly clear in the race to deploy AI at scale: Companies that can see what their agents are doing will move faster than those flying blind. In the emerging era of autonomous AI, observability isn&#x27;t just a nice-to-have feature. It&#x27;s the difference between cautious experimentation and confident deployment—between treating AI as a risky bet and managing it as a trusted workforce. The question is no longer whether AI agents can work. It&#x27;s whether businesses can see well enough to let them.

أطلقت Salesforce مجموعة جديدة من أدوات المراقبة تسمى Agentforce Observability، تهدف إلى تعزيز الشفافية في عمليات اتخاذ القرار للذكاء الاصطناعي. تتيح هذه الميزة للمنظمات تتبع إجراءات وعمليات تفكير وكلاء الذكاء الاصطناعي في الوقت شبه الحقيقي، مما يعالج المخاوف بشأن غموض الأنظمة المستقلة. يعكس هذا الإطلاق حاجة متزايدة للشركات لفهم والتحكم في تقنيات الذكاء الاصطناعي أثناء سعيها لتحقيق مكاسب في الكفاءة.

Salesforce ha lanzado una nueva suite de herramientas de monitoreo llamada Agentforce Observability, destinada a mejorar la transparencia en los procesos de toma de decisiones de la IA. Esta función permite a las organizaciones rastrear las acciones y el razonamiento de los agentes de IA en casi tiempo real, abordando preocupaciones sobre la opacidad de los sistemas autónomos. El lanzamiento refleja una necesidad creciente de las empresas de comprender y controlar las tecnologías de IA mientras buscan ganancias de eficiencia.

Salesforce a lancé une nouvelle suite d'outils de surveillance appelée Agentforce Observability, visant à améliorer la transparence des processus décisionnels des IA. Cette fonctionnalité permet aux organisations de suivre les actions et le raisonnement des agents IA en temps quasi réel, répondant ainsi aux préoccupations concernant l'opacité des systèmes autonomes. Ce lancement reflète un besoin croissant des entreprises de comprendre et de contrôler les technologies IA tout en recherchant des gains d'efficacité.

Salesforce has introduced a new suite of monitoring tools called Agentforce Observability, aimed at enhancing transparency in AI decision-making processes. This feature allows organizations to track the actions and reasoning of AI agents in near-real time, addressing concerns about the opacity of autonomous systems. The launch reflects a growing need for businesses to understand and control AI technologies as they seek efficiency gains.

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