AI customer service startup Decagon held early buyout talks with ServiceNow. Decagon, valued at $4.5 billion after a January Series D and a March tender offer, counts Duolingo, Notion, and Hertz among its clients, Business Insider reported on the preliminary ServiceNow discussions. Decagon has not even finished spending its last funding round, and buyers are already circling. Business Insider reported on October 13 that the AI customer service agent startup held early M&A talks with ServiceNow and other potential acquirers. Decagon, the three-year-old company that builds AI agents to handle customer support calls and chats for brands like Duolingo, Notion, Hertz, and Deutsche Telekom, has been in early acquisition discussions with ServiceNow and other suitors, Business Insider reported Tuesday. The talks are preliminary, according to the report, but their existence alone says something about where the AI agent market stands right now: enterprise software giants would rather write a check than spend years building the same technology in-house. The timing is what makes this notable. Decagon closed a $250 million Series D in January led by Coatue Management and Index Ventures, tripling its valuation to $4.5 billion, according to Bloomberg. In March, the company completed a tender offer at that same $4.5 billion mark, TechCrunch reported, letting early employees and investors cash out some shares. That is not the profile of a company that needs to sell. It's the profile of one that doesn't have to, and is still getting calls anyway. ServiceNow has spent 2025 and 2026 buying its way into agentic AI rather than building every piece of it from scratch. The company agreed to buy Moveworks, an AI assistant maker, for $2.85 billion in March 2025. It also bought the AI-native conversation analysis platform Cuein. And after Bloomberg reported that it was in advanced talks to buy cybersecurity firm Armis, ServiceNow announced the $7.75 billion deal and completed it in April 2026. Bloomberg has described the pace as a deja vu of McDermott's years running SAP, where acquisitions were also the default growth lever. Decagon would fit a clear gap. ServiceNow sells workflow software to the same enterprise IT buyers who are now being pitched standalone AI customer service agents, and Decagon said it added more than 100 enterprise customers in 2025, spanning travel, financial services, health, and retail. Buying that customer base and the underlying agent technology is faster than spending years rebuilding Decagon's product work on voice, chat, email, and SMS support from scratch. ServiceNow reported Q2 2026 revenue beating the $3.92 billion consensus estimate, with multi-product Now Assist AI deals growing nearly 70% year over year. CEO Bill McDermott reiterated the company's $1.5 billion AI annual contract value target for 2026, giving enterprise software bulls their clearest data point yet that AI is producing durable... - ServiceNow Q2 earnings beat revenue - AI deals driving software revenue growth Decagon isn't the only one proving this thesis. NiCE bought the AI customer service company Cognigy for $955 million, a deal that, like the ServiceNow-Moveworks transaction, signaled that legacy contact-center and workflow vendors see AI agents as something to acquire rather than compete against from scratch. What this means for the rest of the AI agent field. Frankly, this is the signal every other AI agent startup founder is watching for. The agentic AI market has been thick with venture money for two years, and a lot of these companies look similar on paper: a slick agent orchestration layer sitting on top of a foundation model, sold into enterprise support, sales, or ops teams. Funding rounds prove investors believe in the category. Early M&A talks prove the giants who actually touch these enterprise customers are also starting to believe in specific companies within it. Decagon, co-founded in 2023 by Jesse Zhang and Ashwin Sreenivas, went from stealth to a $4.5 billion valuation in under two years, according to the company's own funding announcements. That speed is exactly why a buyer would rather move now than wait. The later a target gets, the more expensive and the more independent it becomes, and the weaker an acquirer's hand is walking into the room. None of this means a deal happens. Early talks fall apart more often than they close, and Decagon, flush with cash from two rounds and a tender offer, has no obvious financial reason to sell. But the fact that ServiceNow is willing to sit down and talk, even informally, tells competitors like Zendesk and the dozens of smaller AI agent startups exactly how this market consolidates when it does: not through slow attrition, but through the biggest platform players buying the sharpest point solutions before their price tags get too big to justify. ServiceNow Ventures has taken a 5% stake in BusinessNext, an Indian AI banking software company, at a $700 million valuation. The $40 million deal is as much a distribution partnership as an investment, tying BusinessNext's AI agents into ServiceNow's AI Control Tower governance platform as the race to monitor and control enterprise agents... - enterprise AI agent governance platform - ServiceNow banking software investment 2026 Join the discussion. No replies yet. Start the discussion. Elroy is a digital marketer and developer from Goa, with over a decade of experience web development and marketing. He has been associated with several startups and serves currently as an Editor to the Asia Pacific Industrial magazine. He occasionally writes on Startup Fortune about technology and automation.

Decagon open-sources PACT protocol to standardize AI agent interoperability. Decagon has released the Personal Agent Consent & Trust (PACT) protocol to standardize how AI agents interact with businesses while ensuring user privacy. AIDeveloper44 Team A conceptual diagram showing the secure, token-based handshake between personal agents and business systems. * Decagon has introduced the PACT protocol to allow secure, authenticated communication between personal AI agents and business systems. * The protocol leverages OAuth 2.0 and Agent2Agent standards to separate agent identity from customer consent. * PACT is being developed as an open standard to facilitate consistent integrations across different AI platforms. The need for standardized agent communication. As the adoption of personal AI agents increases, these systems are beginning to handle more complex tasks on behalf of users, such as booking appointments, managing account subscriptions, and negotiating purchases. Previously, these interactions often mimicked human behavior by driving web interfaces, which presented security risks and authentication challenges. Brands struggled to verify the identity of the AI agents contacting them, and users were forced to share credentials to enable automated actions. To address these challenges, Decagon has open-sourced the Personal Agent Consent & Trust (PACT) protocol. Developed in collaboration with Instinct, the protocol aims to provide a secure framework for personal agents to interact with the support infrastructure of businesses. By creating a standardized interface, PACT allows businesses to verify which platform is acting on behalf of a user while ensuring that sensitive login information remains private. Protocol design and architecture. The PACT protocol is built upon existing technologies, specifically the Agent2Agent (A2A) protocol and OAuth 2.0. This design choice enables developers to use established standards for identity and authorization. According to the technical documentation, the protocol functions through three distinct parties: the personal agent platform, the business (Brand), and the provider hosting the support agent. The interaction process is broken down into three logical phases: * Discovery and Connection: Personal agents discover a business's "Agent Card," which lists the endpoint and available permissions (scopes). The personal agent then authenticates its requests using a signed JSON Web Token (JWT). * Consent and Authentication: Rather than sharing passwords, users sign in directly with the business. The business then issues a delegation token that specifies the exact permissions granted to the agent. * Execution: The personal agent presents the delegation token alongside its identity, allowing the business's agent to perform actions strictly within the pre-defined constraints. Separating identity from authority. A key principle behind PACT is the separation of identity from authority. The protocol enables a business to verify that a request is coming from a legitimate, trusted personal agent independently of whether that agent has been granted specific permissions to modify a user's account. This decoupling allows for more granular security policies, as businesses can define their own authorization scopes, such as read-only access to order history versus the ability to perform cancellations. Industry collaboration and adoption. Decagon has stated that PACT is intended to be an open industry standard. The company has joined the Personal Agent Protocol working group, which was previously announced by the development team behind Muse. By publishing the specification and code on GitHub, the organizers aim to encourage adoption among other personal agent developers, support vendors, and businesses. The goal is to ensure that a user's choice of personal agent is not limited by the platforms a specific business chooses to support. By providing a consistent integration pattern, PACT seeks to reduce the friction developers face when building cross-platform agent systems. Whether acting as a personal assistant or an automated concierge, agents using PACT can operate with verified credentials while adhering to business-side security and policy constraints. References & Sources

Decagon launches AI for the era of personal agents at Dialogues. Search. New products for the next frontier headline the company's second annual flagship conference, plus new partnerships and industry-leading speakers Decagon, the leader in conversational AI agents for concierge customer experiences, today hosts its second annual flagship conference, Decagon Dialogues, ushering in an era of AI that meets customers - and their agents - however they choose to engage. With Voice 3, Personal Agent Gateway, Agent Modules, and Duet Apprentice, Decagon is meeting the moment where customers expect to be treated like someone the business already knows, not a ticket number in a queue. Frontier AI models are intelligent about the world in general, but a model that knows the world is not the same as one that knows a specific business: its policies, its systems, its customers, and the knowledge that used to live in one person's head. Closing that gap is what Decagon spent the last year building toward, and today's product launches are the result. Frontier models keep improving, but still don't account for the nuances of how an agent should sound on the phone, how it should interact with a customer versus that customer's personal agent, how a bank conducts a fraud dispute versus how an airline rebooks a stranded passenger, or how a company onboards and trains its best teammates. Each product announced today delivers that expertise: * Voice 3 knows customer experience, combining a new duplex architecture with Chord, the first voice model from Decagon Labs, for more natural customer conversations. Most voice agents combine general-purpose speech models with cascaded pipelines that struggle to coordinate conversation with ongoing work. Customers hear the result in stiff delivery and awkward pauses or interruptions. The Chord voice model is built for the intonation and natural pauses of a live call, slowing down for details like confirmation codes, then returning to a conversational pace. Voice 3 also introduces a new duplex architecture where conversation and task execution run in parallel, letting the voice agent narrate its progress and answer follow-up questions during a long-running task, rather than putting the customer on hold. * Personal Agent Gateway knows your customers however they arrive, helping you identify and serve personal agents on your terms.Personal agents like Muse, Instinct, and dots now book, buy, cancel, and negotiate for customers, and they are already showing up in support queues. Personal Agent Gateway detects whether a human or a personal agent is on the other end, using signals from your business, such as account history, and from the platform, like request cadence. Personal agents are routed to a dedicated channel where your agent resolves requests directly in a fraction of the time and cost. Personal Agent Gateway authenticates the agent, limits what it can do, and requires approval from the owner behind it for sensitive actions, declining if they can't be reached. * Agent Modules know your industry, extending your agent across the customer journey in financial services, travel and hospitality, healthcare, retail, technology, and telecom.Expanding beyond traditional support requires teaching your agent new workflows, requirements, and measures of success. Instead of requiring teams to build the infrastructure for each journey from scratch, modules bring together the orchestration each journey requires, adapted to your specific processes and business context. As modules extend your agent across more journeys, a shared management layer lets each team manage its part of the agent with separate access controls, while giving admins visibility across the whole customer relationship. Memory carries across conversations and channels, so the customer from a sales call last month is recognized when the onboarding call begins. * Duet Apprentice knows your business, enabling your agent to learn like your best people and keep pace as your business changes. With Apprentice, Duet studies your existing internal resources and learns from the escalated conversations your experienced reps handle. That knowledge grounds how it drafts Agent Operating Procedures (AOPs), runs Simulations, and analyzes conversations. Duet also keeps learning and working alongside your team after launch, following Slack and Microsoft Teams discussions as policies and plans change. Tag Duet into a thread and it can draft the AOP updates; when something is ambiguous, it proactively asks for clarity in the same channel. What Duet learns belongs to you, stays in your workspace, and isn't used to train models outside it. Frontier models know the world. They do not, on their own, know any one business, and that is the gap Decagon exists to close. "The promise of AI within customer experience is not just a support channel that happens to use AI, but a singular 'front door' for customers to interact with a business and carry them through the entire journey," said Jesse Zhang, Co-founder and CEO of Decagon. "With these new products and partnerships, we're bringing this vision to reality." Speakers across industries share how Decagon changed their businesses. This year's Dialogues lineup brings together experts from retail, healthcare, travel and hospitality, insurance, finance, and technology, all using Decagon to deliver concierge experiences. Select speakers include: * Eric Phillips, Senior Vice President and Chief Digital Officer, Delta Air Lines, joins the opening keynote to discuss how AI is reshaping customer experience at one of the world's largest airlines. * Samuel Liyanage, Vice President of Digital Customer Experience, and Misty Shafer Sterne, Vice President of Commercial Technology, both of American Airlines, explain how to design AI-powered journeys that build trust and loyalty. * Adam Terk, Vice President of Operations, Carrot, connects the healthcare experience across every touchpoint a patient has with the business and shares the outcome. * Diana Rainesh, Senior Vice President of Strategy & Analytics Support & Operations, Ticketmaster, describes using AI to power product discovery and personalization at scale. Partnerships take customer service to the next level In addition to recently announced partnerships with companies like OpenAI and AWS, Decagon continues to expand its ecosystem capabilities: * Decagon is partnering with Databricks to bring governed enterprise data into every customer conversation. Many of the most useful answers a Decagon agent could give - order status, entitlements, product usage, payment history - live in the Databricks Data + AI Platform rather than the conversation itself. Historically, surfacing that context meant building and maintaining bespoke data pipelines, with sensitive customer data copied into a new system each time. Working with Databricks, Decagon agents now read that record directly, so agents act on real-time customer context with no ETL and no new copy of the data. Conversation data flows back the same way, enriching Databricks tables with intent, sentiment, and root-cause insights. The partnership will also extend to inference: Decagon has joined Databricks' Built on program and will route model calls through Unity Gateway, so governance, lineage, and audit trails cover agent activity the same way they cover the rest of a customer's data estate. Decagon is also a launch partner on Databricks Marketplace, letting customers purchase Decagon through their existing Databricks relationship. * Decagon and Plaid are partnering so AI agents can complete complex financial tasks directly within a conversation. Many of the most common requests in financial services require the customer to log into their bank first. That means sending them outside the conversation to authenticate their account, which breaks the conversation and often ends it. Working with Plaid, Decagon agents can resolve account issues in real time, within the agent conversation. Consumers can complete a claim, recover a failed payment, manage applications, and handle disputes with full context from prior conversations, including the steps that require financial account access. The partnership ensures trust and safety, operating at the level of oversight financial services require. Plaid powers consumer-permissioned account access, and agent guardrails stay exactly as Decagon's financial services customers already run them.