Google Cloud has partnered with Parallel Web Systems to natively integrate Parallel's search infrastructure as a web grounding provider on the Gemini Enterprise Agent Platform. This integration enables developers to anchor their AI agents in verifiable, real-time web results, significantly improving factual accuracy for complex enterprise workflows. Additionally, the partnership offers expanded architectural flexibility, allowing users to programmatically extract, permanently cache, and process web data alongside other large language models.
Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google's accelerators using the familiar Ray task-and-actor APIs. To handle the strict networking requirement of keeping multi-host TPU "slices" together over their Inter-Chip Interconnect (ICI), the KubeRay Operator on GKE automatically provisions and labels the underlying hardware layout. Ray Core utilizes these labels via its slice_placement_group() primitive to atomically reserve complete slices, allowing developers to deploy jobs through KubeRay, Ray Train, or Ray Serve simply by declaring a hardware topology (like "4x4") without writing custom placement code.
HeyGen ported their 18B+ parameter Avatar IV video generation model to Google Cloud's Trillium (v6e) TPUs via torchax and XLA, utilizing FSDP and Ulysses sequence parallelism across an eight-chip mesh. To achieve a 1.86x speedup for real-time streaming, the engineering team pipelined exposed all-to-all collectives, aligned sparse attention block sizes to eliminate mask padding, and bypassed softmax serial dependencies using a precomputed Cauchy-Schwarz upper bound. These custom Pallas kernel and compiler optimizations were deployed only after passing rigorous two-tier quality gates to guarantee byte-identical or mathematically equivalent pixel outputs.
Google Cloud API Gateway now offers a model routing feature in Public Preview, allowing developers to dynamically route traffic to models like Gemini, Claude, or OpenAI OSS-GPT without hardcoding endpoints or managing open-source proxies. Developers can easily configure these routing rules directly within their OpenAPI 3.x specifications by mapping virtual model names to specific backend targets on a shared host. Once deployed, the Gateway acts as a serverless ingress layer that accepts standard OpenAI-compatible requests, automatically transcodes the payload to the native schema of the target model, and routes the traffic on the fly.
The 2026-07-28 Model Context Protocol (MCP) specification replaces legacy stateful constraints with a fully stateless core, enabling cloud-native horizontal scaling, serverless deployments, and standard round-robin load balancing. This architectural shift introduces standardized HTTP headers for efficient routing without deep packet inspection, caching controls, and Multi Round-Trip Requests (MRTR) to handle interactive and long-running tasks without blocking connections. Developers can immediately begin migrating their agentic applications to this highly scalable infrastructure using the newly available beta SDKs for Python, TypeScript, Go, and C#.
Credentio is a newly released, open-source C++ library from Google that allows developers to integrate high-performance, local-first validation of C2PA Content Credentials into their client and server applications. By processing assets entirely locally with a highly optimized memory footprint, the library delivers instant validation verdicts for multi-gigabyte media files without incurring cloud latency, bandwidth costs, or data privacy risks. The library currently features deep manifest parsing alongside configurable trust list integration, and is available now on Google Source with future plans to support full credential generation and embedding.
Adding an AI agent to Slack sounds appealing to many enterprises — but, as VentureBeat has experienced ourselves first hand — the reality is often far more complex and clunkier than it first seems. Now NanoCo ., the company behind the hit open source, enterprise-friendly, autonomous AI agent harness NanoClaw (a more sandboxed, lower code version of OpenClaw), is hoping to make it just as easy as typing a Slack message. To go one step further: the company's new NanoClaw Slack integration lets human users spin up entire teams of agents with their own specialized skills, workflows, and even custom avatars, all from a single Slack prompt. "In the next 12 to 18 months, everyone on a team will be a manager of agents," NanoCo CEO and co-founder Gavriel Cohen told VentureBeat in an exclusive interview. Furthermore, the NanoClaw agents can work together in channels and shared Slack Canvases, and can even be messaged outside of Slack on other platforms like Telegram or WhatsApp, letting their human colleagues ping them across messaging platforms, just as they would their fellow humans. “I think this is agents arriving natively in Slack for the first time,” Cohen added. “In the past, you had to do all these weird things to try to have multiple different agents behind the scenes using the same bot, and now every agent gets its own identity in Slack — its own avatar, its own face, its own name. You can tag them. They can tag each other.” For enterprise teams, the more consequential part is persistence and separation. NanoClaw is not presenting the additional workers as invisible subagents that disappear after one task. Each can be given its own role, memory context, instructions and permissions, creating a structure closer to a small digital department than a single chatbot with a long prompt. As with the original open source version of NanoClaw released in January 2026 , developers and enterprises can further choose whichever underlying large language model (LLM) they wish to power their NanoClaw agents, optimizing for performance, cost, or other combinations of factors. From a single NanoClaw Slack agent to a whole specialized team For a new installation, NanoClaw’s current setup process starts by cloning the project and running its nanoclaw.sh installer, which walks the user through dependencies, credentials, building the agent container and pairing a first messaging channel. NanoClaw’s website says the installer takes a user “from a fresh machine to a named agent you can message,” with Slack among the supported channels. Cohen described the Slack-specific flow to VentureBeat as a significant simplification over building a traditional Slack bot. Previously, he said, a user would have to navigate Slack’s administrative and developer interfaces, create an app, collect secrets, API keys and tokens, and then move those credentials into wherever the bot was running. With the new integration, the NanoClaw setup instead offers a Connect Slack option. The user names the agent, authenticates, chooses the NanoClaw Add to Slack option and goes through Slack’s installation and authorization flow. Once authorized, the first agent can appear in Slack and begin communicating with the user. The important distinction is that this initial authorization is largely a one-time workspace connection. Slack’s Marketplace listing says users “connect a workspace once,” after which NanoClaw can provision each additional agent as its own Slack bot, complete with its own name, generated avatar and identity. Those agents continue running on the customer’s infrastructure and connect to Slack over Socket Mode. NanoCo says it does not store the agents’ Slack tokens; according to the Marketplace listing, those tokens remain on the user’s machine. Slack’s standard administrative controls still sit around that system. Organizations can apply their normal app-approval policies to the NanoClaw integration, while NanoClaw’s Marketplace listing says the app’s Home tab displays the agents provisioned in a workspace and lets users revoke individual agents or disconnect the workspace entirely. The result is less a one-click replacement for NanoClaw’s underlying infrastructure than a one-time bridge between that infrastructure and Slack: users still own and operate the agent runtime, but once the bridge is authorized, the agents themselves can create and coordinate additional Slack-native colleagues without sending the user back through manual app configuration each time. Behind the scenes, Cohen said, the lead agent has a Model Context Protocol (MCP) tool that can create new agents and define their instructions, personas, skills and tools; another tool can place them into shared rooms. The agents come prepared to work with Slack Canvas and can communicate with every human user on the Slack Channel, and with one another. The interaction itself is deliberately simple. Rather than opening a separate agent builder every time a new role is needed, Cohen said users can tell the agent they already have what kind of colleague or team they want. “Your agent in Slack, you can say, ‘Create me another agent to handle my code reviews. Create another agent to review the contributor articles. Create a team of agents that reviews contributor articles from different perspectives.’ And then your agent can create new agents, and they just pop up in the sidebar and send you messages.” That means a developer could ask for a product manager, architect, implementation agent, code reviewer and testing agent, then give each a different toolset and have them hand work between one another. Cohen said the testing agent, for example, could have access to a testing environment while the review agent carries code-review-specific skills and the product agent monitors user feedback. Cohen argues that this division of labor is more than cosmetic role-playing. “There are advantages in terms of giving each one specific skills, instructions, and tools for different tasks,” he said. “I can have, let’s say, a code review agent, a code testing agent, a code writing agent, and I can have them in a loop.” If the implementation agent runs into an ambiguity, he added, it can tag the product or architecture agent for clarification rather than forcing one general-purpose model to hold every responsibility and tool in the same context. Agents work together with humans on a share Slack Canvas A supplied demo screenshot shows the same pattern applied to marketing: a lead agent named Nano creates Atlas for strategy, Sage for content, Echo for social, Scout for outreach and Compass for SEO and analytics. The agents introduce themselves in the same Slack conversation and begin coordinating work, with Atlas noting that it had added an item to Canvas so the task would not get lost. Users do not have to specify every detail up front. Cohen said someone could give the lead agent exact review procedures, priorities and required tools, or leave more of the configuration to the agent based on its existing context and memory. The design also tries to avoid a familiar multi-agent failure mode: bots endlessly triggering one another. NanoCo says the agents reply only when tagged, while comments left on work in Canvas can be routed back to the agent responsible for that piece. And the model can extend beyond teams of task-specific bots created by one person. Cohen described a workplace where individual employees each have persistent agents that can communicate with one another under human-defined policies. “Each person having their own agent means that I could have my agent and you have your agent in Slack, and your agent can ask my agent questions,” he said. “Maybe I’m out of the office for the day. Your agent can ping my agent and ask a question about availability, and I can set some policies about whether my agent can answer or if I need to give approval.” That pushes the concept closer to organizational delegation: some agents specialize by function, while others effectively represent individual employees and the context they have accumulated. Cohen said the agents can be equipped with browser and internet access, memory, coding capabilities and other tools, while newly created agents arrive with built-in support for Canvas work, agent-to-agent communication and spawning still more agents. Slack is opening the door to more third-party agents The underlying Slack change is broader than NanoClaw. In April, Slack, a Salesforce product, announced the ability to add external AI agents to the messaging platform directly, initially pointing to Vercel and Lovable and saying those integrations were coming in late May. Slack said the deployment mechanism automates OAuth, manifest configuration and environment setup so an externally built agent can be brought into the workspace without being rebuilt specifically for Slack. Salesforce’s newly published Slack Code page now names NanoClaw alongside Lovable, Hyperagent, Superhuman, n8n, Vercel, ChatGPT, LangChain, Runlayer and Skydive, and says Add to Slack can bring agents from those platforms into Slack in a few clicks with their own identity. Slack is already crowded with AI assistants. OpenAI, for example, lets ChatGPT workspace agents be deployed into Slack channels, where they can answer questions, perform tasks through connected systems and output files. Slack also supports Claude and custom Agentforce agents. NanoClaw’s differentiation is therefore not simply “AI in Slack.” It is the ability for an already-running agent to create additional, independently addressable teammates from inside the conversation itself. NanoCo calls that a first for Slack; that specific market-first claim is the company’s. “Add to Slack means one message can spin up a full team of NanoClaw agents, working right alongside people in Slack,” Josh Milas, director of product management at Slack, said in the supplied announcement. How NanoClaw differs from Claude Tag, ChatGPT agents and Agentforce in Slack NanoClaw is not alone in trying to turn AI from a sidebar chatbot into something resembling a persistent Slack colleague. Anthropic’s Claude Tag , which began rolling out in beta to Claude Team and Enterprise customers in June, may be the closest conceptual comparison. Administrators can give @Claude access to selected channels, tools, data sources and codebases; everyone in the channel can then delegate work to it by tagging it. Claude remembers relevant information from the channels it inhabits, can work asynchronously over hours or days, and, when administrators enable its “ambient” behavior, can proactively flag information or revive unresolved work without waiting for another prompt. Anthropic says separate Claude identities can also be scoped to different use cases so that, for example, a sales Claude does not share its memories or tools with an engineering Claude. The difference is in how those digital coworkers are provisioned and organized . Claude Tag’s documented workflow is administrator-led: admins pair Claude with Slack, decide which channels, tools and information each Claude identity can access, set spending limits and then expose those identities to employees. Within a given channel, Anthropic describes “one Claude that interacts with everyone.” Its public documentation does not describe an end user asking that Claude to create several new, independently named Slack bots on demand. NanoClaw’s model is almost inverted. After an organization connects its NanoClaw installation to Slack once, NanoClaw says an existing agent can itself provision additional agents from a conversational request, with each new worker receiving its own Slack bot identity, name, generated avatar and token and running back on the customer’s infrastructure. OpenAI’s ChatGPT Workspace Agents occupy another point on that spectrum. Business, Edu and Enterprise customers can build reusable agents in ChatGPT, give them instructions, models, files, apps, custom MCP connections and schedules, and then attach those agents to Slack channels. Builders assign each agent a unique Slack handle and can configure it either to respond only when mentioned or to respond automatically to relevant messages in a channel. But the construction still happens primarily through ChatGPT’s agent builder: OpenAI’s setup documentation tells users to create the agent first and then add Slack as a channel. Under the hood, the Slack handles rely on Slack user groups managed by the ChatGPT Agents app, rather than NanoClaw’s model in which every provisioned agent is itself a separate Slack bot. Salesforce’s Agentforce similarly allows organizations to create multiple specialized agents that employees can DM or @mention inside Slack, and it arguably provides the most conventional enterprise administration model of the group. Companies build the agents in Agentforce Builder, often starting from Slack-specific templates for jobs such as customer insights, employee help or onboarding, and can add subagents and actions that let them search information, create Canvases or perform other work. Once configured and activated in Salesforce, administrators bring those agents into Slack for employees to use. That makes Agentforce powerful for organizations already centering identity, data and workflows on Salesforce, but again places agent creation before deployment rather than making creation itself something an existing Slack agent can perform during a conversation. That distinction helps clarify what NanoClaw is actually adding to an increasingly crowded market. Slack itself now provides an Agent Kit for developers and a deployment standard for agents built on outside platforms, automating pieces such as OAuth, manifests and environment configuration. Claude Tag, ChatGPT Workspace Agents and Agentforce all demonstrate that persistent, specialized AI teammates inside Slack are no longer novel on their own. NanoClaw’s more unusual bet is recursive provisioning: Slack becomes not merely the place where workers invoke agents, but a place where an existing agent can assemble additional named agents, assign them roles and put them together in a channel as a working team. There are tradeoffs to the different approaches. Claude Tag comes with Anthropic-managed models and centralized administrative controls, including channel-specific permissions, audit logs and token-spending limits, while also offering proactive “ambient” behavior that NanoClaw’s supplied materials do not claim in the same way. ChatGPT Workspace Agents offer a managed agent builder, schedules, app connections and organization-level publishing and access controls. Agentforce ties agents closely to Salesforce permissions, enterprise data and predefined business actions. NanoClaw instead emphasizes self-hosting, open-source modification and separate agent identities , shifting more control — and more operational responsibility — to the organization running it. The result is less a direct replacement for those systems than a different answer to the same emerging question: whether enterprises want a small number of centrally configured AI assistants, or an environment in which employees and existing agents can continuously create specialized digital colleagues as new work appears. How NanoClaw got here NanoClaw began far from the enterprise collaboration market. Cohen, a former Wix engineer, launched it under the MIT License on Jan. 31, 2026, as a deliberately small, security-focused alternative to OpenClaw. The original pitch was that a personal agent with access to messages, files and tools should run inside an OS-isolated container rather than directly on the host, and that the orchestration layer should remain small enough for a developer or security team to understand — an initial core of roughly 500 lines of TypeScript and a design centered on container isolation and a minimal single-process architecture. The project then moved steadily toward enterprise infrastructure. In March, NanoClaw partnered with Docker to run agents inside Docker Sandboxes, using stronger MicroVM-backed isolation for workloads that may install packages, modify files and launch processes. In April, NanoClaw 2.0 added Vercel’s Chat SDK and OneCLI’s credential gateway, allowing organizations to define policies around sensitive actions and require human approval before credentials are injected for protected requests. By May, Cohen and his brother Lazer Cohen had formed NanoCo around the project and raised a $12 million seed round led by Valley Capital Partners, with Docker, Vercel, monday.com and others participating. The commercial strategy is to keep NanoClaw open source while selling managed, organization-wide deployments and “professional assistant” infrastructure to enterprises. The company now says NanoClaw has surpassed 250,000 downloads and 30,000 GitHub stars. That open-source structure remains central to Cohen’s pitch as NanoClaw moves deeper into workplace infrastructure. “You’re really able to now integrate an open-source agent into Slack that you fully control,” he said. “You can change all those configurations. Plus, you can fork NanoClaw and completely rewrite or change behaviors — create your own memory system, your own coding harness, agent harness. Whatever you want to do, you can do. Total freedom.” Persistent agents, but infrastructure stays under the user’s control Cohen said NanoClaw remains self-hosted: an organization can run it on a local machine or its own cloud VM, with agent data stored there. The same agent can also appear across Slack, WhatsApp or Telegram while retaining the same memory, workspace and tools, although each messaging surface uses a separate session. NanoClaw can pull recent context across those sessions so the agent can maintain continuity without merging every chat history into one stream. NanoClaw’s documentation likewise describes a multi-channel architecture in which the same agent can retain one workspace and memory while maintaining separate per-channel sessions. “This is all self-hosted,” Cohen said. “You’d be running this on your computer or on your virtual machine in the cloud, and that data is stored on your computer or on your [virtual machine] VM. This could be an open-source model running on your Mac Mini, and your data isn’t going anywhere besides your Mac Mini and then into Slack.” The cross-channel continuity is also intended to make an agent feel less like a Slack-specific bot and more like a persistent colleague that happens to be reachable through Slack. Cohen said the same agent could exist in Telegram, WhatsApp and Slack with access to the same memory, files and tools. The conversations remain separate sessions, but they share a workspace and persistent context so the agent can carry knowledge from one surface to another. That architecture matters when an organization starts creating many agents. Cohen said one agent can see its own sessions across channels, but not another agent’s private sessions by default. NanoClaw’s current documentation likewise describes agents running in their own sandboxes and configurable model providers, with Claude Code as the default and Codex, OpenCode and local Ollama models available as alternatives. There is one cloud dependency for the new Slack flow. Cohen said NanoCo operates a small service that handles Slack provisioning requests and avatar generation. He said it does not receive users’ messages or agent memory. Continued commitment to open source NanoCo is not charging for this community Slack capability, according to Cohen, and is absorbing the provisioning-service and avatar-generation costs. Users can still incur their own model inference and hosting expenses, so that does not make a deployed agent team cost-free in practice. NanoCo says the integration is available through the Slack Marketplace, subject to normal workspace app approval and governance. Slack says workspace owners and administrators can require apps to be approved before installation. Cohen framed that decision as part of NanoCo’s broader open-source strategy rather than a standalone monetization play. “We’re not making any money off this one. This one is for the community, really,” he said. “We know that in the long run that’s going to benefit NanoCo as a company. As NanoCo grows and builds out capabilities, those go back to the open source. I think that’s the new model of open source, where we’re not trying to monetize every bit of value we bring to the community.” Whether companies get there that quickly will depend less on how easily agents can be created than on whether IT teams can govern their permissions, memory, spending and failure modes at the same pace. NanoClaw is betting that the next problem is managing the digital coworkers that appear once that barrier is gone.
Serval is making Catalyst , its AI agent for building enterprise automations, generally available Thursday and enabling it by default for customers — allowing teams of AI agents to decide what should be automated and then build the automation itself. Catalyst sits above Serval’s AI-native service management platform as an admin-facing “super agent.” It can inspect ticket history, standard operating procedures or natural-language instructions, identify recurring work, and draft the workflows, skills, forms, access policies, journeys and dashboards needed to automate it. Serval is also using Catalyst to create background agents that continuously inspect connected systems for emerging problems and propose fixes before an employee files a ticket. That distinction matters because enterprise service management vendors are rapidly converging on AI-assisted workflow creation. ServiceNow’s Build Agent can already translate natural-language instructions into full-stack applications, flows, scripts and other platform metadata, while its AI Agent Advisor can analyze instance records to identify automation opportunities. Atlassian’s Rovo can generate Jira automation flows from plain-English requirements, and Freshworks offers Freddy AI Agent Studio for creating service agents that act across Freshservice workflows. So Serval’s claim to differentiation is narrower — and potentially more consequential — than simply “we use AI to build workflows.” Catalyst is designed as a single administrative layer that can move from discovering an opportunity, to assembling multiple kinds of governed automation, to creating proactive agents that keep looking for new work to automate. "You just started with a single prompt, and now you’ve got enterprise-grade workflows ready to deploy that are going to solve all password resets for the entire company," Serval co-founder and CEO Jake Stauch told VentureBeat in an interview. From ticket history to working automation Serval says Catalyst analyzes existing help desk data before an organization has decided what to automate. If it finds a repetitive category of requests, it can draft the automation required to resolve those requests and stage the result for administrator review. Users can also upload an SOP or spreadsheet and ask Catalyst to turn the documented process into an executable system. Serval’s documentation says Catalyst can build workflows, author help desk skills, create onboarding and offboarding journeys, configure access-management policies, construct dashboards, investigate operational issues and debug failed workflow runs. Unlike Serval’s earlier workflow builder, Catalyst is intended to become the primary interface for configuring the platform; the company says its long-term goal is that anything an administrator can do through the UI should also be possible through Catalyst. The actual workflows are code-backed. In a demonstration, Stauch showed Catalyst taking a request to build password-reset workflows, detecting connected systems including Okta, Google Workspace and Microsoft Entra, and generating the underlying TypeScript needed to perform those actions. Administrators could then add approvals or restrict who was allowed to run the workflow. The models underneath Catalyst are deliberately swappable Serval is not building its own foundation model. Stauch said in the interview that the company uses models from “frontier labs,” runs evaluations to determine which models work best for particular jobs, and is deliberately model-agnostic. “You can swap different models in,” he said, adding that Serval also works with enterprises that build their own models. Stauch provided more detail in a May 2026 interview with Sequoia Capital , saying Serval was using both OpenAI and Anthropic models. He said OpenAI’s GPT models had performed best for end-user interactions and tool calling, while Anthropic’s Sonnet and Opus models were producing the strongest results for the code-generation side of Serval’s automation system — the workload most directly relevant to Catalyst. Serval continuously runs evals rather than automatically moving every workload to the newest model release, Stauch said. That architecture makes the underlying LLM less central to Serval’s differentiation. The company’s own documentation now lets organization administrators supply their own OpenAI or Anthropic API keys, including a compatible custom endpoint, while Stauch said the broader architecture can accommodate different models. The materials do not, however, establish that every Catalyst user gets a self-service menu for arbitrarily choosing an individual model. Serval’s pitch is instead that its proprietary value sits in the harness around those models: enterprise context and memory, integrations, generated code, permissions, approvals and the controls governing what an agent can actually do. That code-generation model is central to Serval’s pitch against ServiceNow. Stauch argues that legacy ITSM deployments often accumulate custom tables, business rules, workflows and platform-specific expertise that make seemingly simple automation changes expensive to implement. Serval, by contrast, wants administrators and business teams to describe the outcome they need and let the model generate the implementation. But ServiceNow is no longer standing still on that front. Its current Build Agent similarly creates applications and code from natural-language prompts, supports flow design and testing, and operates inside ServiceNow’s governance framework. ServiceNow’s AI Agent Studio lets customers create agents and agentic workflows, while AI Agent Advisor is explicitly designed to analyze operational records for automation candidates. The competitive question is therefore shifting from “who has generative AI?” to how many separate tools, configuration concepts and specialists are required to get from an observed operational problem to a production automation. Serval is effectively arguing that Catalyst compresses those steps into one conversational surface and a smaller platform model. ServiceNow, by comparison, now has a powerful but broader set of AI and development surfaces spanning Build Agent, AI Agent Studio, AI Agent Advisor, Workflow Studio and AI Control Tower. That breadth is an advantage for customers already deeply invested in ServiceNow, but it also illustrates the complexity Serval is attacking. ServiceNow itself notes that Build Agent is aimed at admins and developers who understand and can support what it generates. Atlassian is moving in the same direction from a different starting point. Rovo can generate “if this happens, then that happens” automation flows from natural-language descriptions, while Jira Service Management increasingly supports agents that triage, investigate and execute service work. Freshworks ’ Freddy AI Agent Studio likewise emphasizes agents that resolve requests end-to-end, with prebuilt IT and HR agents and more than 30 workflow templates. Catalyst’s differentiator, then, is not that rivals cannot generate an automation from a sentence. It is Serval’s attempt to make the entire automation lifecycle itself agentic. Building agents that look for trouble before a ticket exists That approach becomes clearest with Serval’s background agents. Rather than waiting for a help desk request, a background agent can run on a schedule across connected systems, correlate signals and draft a remediation. In one customer example provided by Serval, an agent correlated network incidents across two offices using switch telemetry, DHCP data and historical tickets, ruled out hardware and wireless interference, traced the issue to configuration drift, and generated a remediation workflow for an administrator to approve. “Most AI agents today wait for an employee to ask a question or submit a ticket,” Stauch said. “We believe the future is AI that acts before an employee ever submits a request.” That framing also highlights a philosophical difference in Serval’s pitch. The startup does not want service management to revolve around creating, routing and tracking better tickets. It wants the system to eliminate as many requests as possible by turning repeated support work into executable automation. "A lot of the code written in enterprises has nothing to do with software engineering," Stauch explained. "It’s actually internal automations and other scripts for the company, and so we use that technology to build a better service management platform." Serval's pitch to enterprises is that it can largely automate those scripts. And the governance model is critical because Catalyst can generate code and potentially initiate changes across production systems. Serval says Catalyst inherits the permissions of the user operating it and remains scoped to that user’s team workspace. Everything it builds starts as a draft, and organizations can restrict publishing privileges or require formal review and approval before an automation becomes active. Customer data remains customer-owned, with several deployment options Those controls also extend to the enterprise data Catalyst examines. Stauch said Serval is intended to operate as the customer’s system of record and told VentureBeat that “they own all the data.” Serval’s current Master Services Agreement is more precise: customers retain rights, title and interest in both their “Customer Materials” — a category that includes records, documents, workflows, prompts, inputs and configurations — and the output Serval generates from them. Serval receives the rights necessary to process that information to provide, maintain, support and secure the service. Serval also says it does not retain or use customer materials, inputs or outputs to train, fine-tune or improve its own or third-party AI models. Its Data Processing Addendum identifies Serval as the processor of customer personal data and allows processing for operating the service, responding to support requests, diagnosing issues and protecting the platform, while authorized subprocessors can also be involved. Serval’s acceptable-use terms say it maintains a current list of AI subprocessors and model providers for customers. Where that data resides can vary by deployment. Stauch said customers can use Serval as a cloud SaaS service, run it on-premises or place it in their own VPC. Serval’s self-hosting documentation now describes two fuller options: a Serval-managed single-tenant deployment inside an AWS account owned by the customer, or a self-managed deployment on the customer’s Kubernetes cluster in any cloud or on-premises environment. In the AWS option, Serval says it operates the installation without persistent IAM access to the customer’s AWS account. There are therefore two distinct access boundaries for enterprise buyers to consider. At the Catalyst level, the agent can only reach data, integrations and automations available to the user and team workspace under which it is operating. At the platform level, Serval and authorized subprocessors necessarily process customer information to deliver and support the service, subject to the company’s contractual confidentiality and data-processing terms. That makes Stauch’s informal statement that Serval “doesn’t touch” customer data better understood as an ownership and deployment claim, rather than a literal assertion that the service never processes it. Ramp and other customers provide an early test Customer deployments provide some evidence that the faster-build thesis can translate into operational changes, although the metrics come from Serval’s own case studies. Corporate expense and financial technology firm Ramp says in a Serval case study that Catalyst has made workflow building 50% faster and helped extend Serval across roughly 10 teams, including IT, finance, facilities, people and talent, legal and business operations. In one hardware replacement program, Serval says Ramp automated 600 laptop replacements and saved 150 hours, leaving approval as the principal human step. The more telling Catalyst example may be what happened afterward. Ramp had already automated laptop replacement when Catalyst suggested splitting its shipping logic into separate office and home workflows to reduce errors. The company also says employees outside IT now use Catalyst for analytics, bulk ticket operations, workflow troubleshooting and HR process automation. Other Serval deployments show the broader operating environment Catalyst is meant to configure. Mercor says it has onboarded more than 4,000 external experts through Serval automations and expanded the platform across seven teams. Together AI says Serval automates 95% of its just-in-time infrastructure access requests, with approval and auditing controls around sensitive access. Perplexity says Serval automatically handles more than half of its incoming IT requests and all employee onboarding. Those deployments extend beyond Catalyst itself, but they demonstrate the type of cross-system automation substrate Catalyst is now being asked to build and maintain. Serval says more than 90% of customers adopted Catalyst as their starting point for automation during beta. Catalyst is generally available Aug. 20 and will be enabled by default for all Serval organizations. Pricing and the battle with ServiceNow Pricing is customized depending on the size of the deployment and is not publicly listed on Serval's website or documentation. Serval describes a single platform fee and typically runs a pilot to determine expected deployment and usage. Stauch said the software license can be similar to ServiceNow’s, but argues total cost of ownership can be substantially lower because customers require fewer implementation and maintenance services. "The total cost of ownership is going to be dramatically less — usually half as much, sometimes 10 to 20% of the total cost of ownership of ServiceNow," Stauch said. "But the actual software license fee is not necessarily going to be all that different." Serval's origin story and history Serval was founded in 2024 by Stauch and CTO Alex McLeod, former Verkada product and engineering leaders, after they repeatedly heard IT customers complain about overburdened help desks and the limitations of established IT service-management software. Serval has positioned itself as an AI-native alternative to platforms such as ServiceNow and Jira Service Management, combining help-desk ticketing, access management, asset management and workflow automation within a single system. Serval and Sequoia Capital describe the company’s goal as moving IT software beyond merely recording and routing requests toward resolving them automatically. The company can operate as an organization’s primary IT service-management system or add automation to an existing one. Its publicly identified customers include Perplexity, Mercor, Clay, Verkada and Together AI. Serval says customers can automatically resolve more than half of their incoming IT requests; its Together AI case study reports automation of 95% of that customer’s just-in-time access requests. Investor interest accelerated rapidly in late 2025. Serval announced a $47 million Series A led by Redpoint Ventures in October, bringing its funding at that point to $52 million. In December, it raised another $75 million in a Sequoia-led Series B at a $1 billion valuation , lifting total capital raised to approximately $127 million; Redpoint, Meritech Capital and General Catalyst also participated. Serval told Reuters that revenue had grown 500% since August 2025 and that it was expanding beyond IT into operational work performed by human resources, finance and legal departments. The big test for enterprise customers For enterprise buyers, Catalyst’s biggest test will be whether its compression of the automation lifecycle survives contact with large, messy, highly customized environments. ServiceNow can now generate applications and discover automation opportunities with AI. Atlassian and Freshworks are adding increasingly capable agentic automation to their own service platforms. Serval therefore cannot rely on natural-language creation alone as its moat. Its stronger wager is that an AI-native platform can make the administrative layer itself agentic: continuously finding repetitive work, building the necessary resources across the service stack, exposing generated code for review, and proposing the next automation before an administrator has opened a workflow designer. If Catalyst works at that scope, the competitive unit is no longer the ticket — or even the workflow. It is the system that keeps turning an enterprise’s operational history into new automation.
NASA astronaut L. Gordon Cooper, Jr. took 29 color photographs of the Earth with a 70mm camera as he orbited our planet during the Mercury-Atlas 9 mission in May 1963. Cooper’s view from the window of his Faith 7 spacecraft was spectacular, and he reported that he could see vehicles motoring on dirt roads, smoke-belching trains, […]
Citrix has released updates to address two security flaws impacting NetScaler ADC and NetScaler Gateway deployments, including a critical-severity authentication bypass vulnerability. According to the cloud computing and virtualization technology company, the issues affect customer-managed NetScaler ADC and NetScaler Gateway, including certain FIPS and NDcPP builds, as well as SecurAccess
It’s a new way into the cloud. GeForce NOW welcomes Firefox support to the cloud, opening up another way to jump into high-performance PC gaming straight from the browser, starting today. Whether on a school laptop or everyday PC, it’s now even easier to play supported PC games without downloading a dedicated app. Plus, discover […]
NVIDIA··3 min read
↘1.5K
💬
Top Discussion
HN
Hacker News
“Kubernetes complexity is pushing teams back to boring servers”
