Long-Running AI Agents Expand Beyond Discrete Tasks to Own High-Value Customer Journeys

Long-Running AI Agents Expand Beyond Discrete Tasks to Own High-Value Customer Journeys AI agents are advancing from handling brief, isolated tasks to managing complex, multi-week 'relay races' across entire customer journeys. By persistently orchestrating tasks in domains such as legal case preparation and consumer finance, long-running agents mitigate handoff drop-offs and enable vendors to transition from cost-saving software licenses to outcome-based revenue models. Traditional high-value workflows operate like relay races, where handoffs across departments and weeks lead to lost leads, neglected evidence, and reduced settlement values. Long-running agents can persistently maintain context, proactively dispatch sub-agents across modalities, and prevent value leakage. Controlling entire journeys enables outcome-based pricing models, though regulatory limits like legal fee-sharing rules and procurement hesitations create friction. Legacy outsourced customer acquisition operators like Red Ventures are facing pressure as AI agents alter discovery and replace manual linear funnels. Go-to-market strategies for long-running agents include selling a software platform, replacing existing outsourced services, or vertically integrating full-stack. From Discrete Interactions to Multi-Step Relay Races Early generative AI deployments succeeded in customer support because narrow, well-defined problem spaces allowed for high reliability and human escalations. However, high-value business processes like plaintiff law case preparation operate as relay races that span weeks or months. In these workflows, prospective clients are easily lost to delayed intake evaluations, evidence collection suffers when staff lack capacity for repeated follow-ups, and fragmented records obscure critical details such as medical indicators that impact settlement values. Persistent Execution and Leakage Reduction Long-running agents address these vulnerabilities by tracking context across extended time horizons and diverse communication channels. Triggered by events or deadlines, a primary agent can deploy sub-agents to contact witnesses repeatedly, draft filings, and maintain comprehensive records across police reports and medical chronologies. By operating without labor constraints, agents evaluate cases instantaneously, examine routine files in exhaustive detail, and continuously improve through cross-case data generation. Outcome Economics and Pricing Friction Owning end-to-end journeys allows AI vendors to capture a portion of topline revenue growth rather than competing purely for internal cost-reduction budgets. Because performance is directly tied to tangible outcomes like legal settlements, vendors can structure contracts around delivered results. However, this commercial shift encounters regulatory and organizational friction: non-lawyer fee-sharing restrictions bar direct legal settlement splits, enterprise procurement teams lack outcome-based precedent, and attribution disputes persist when agents interface with internal staff. The Evolution of End-to-End Business Models Historical outsourced service operators like Red Ventures proved the value of monetizing full journey ownership, generating approximately $370 million in EBITDA on $1.4 billion in sales in 2022 by closing insurance contracts. However, consumer shift toward AI agents for discovery and the rise of persistent automated workflows threaten that model, contributing to an estimated EBITDA decline to roughly $160 million by 2025. Go-to-Market Pathways Commercializing long-running agents can follow three primary structures: selling the software platform to existing enterprise teams, selling an outsourced service directly into pre-existing business process outsourcing (BPO) budgets, or adopting a full-stack vertically integrated model. While full-stack operations avoid attribution friction and capture maximum upside, they require managing compliance, regulatory licenses, and operational burdens outside the core AI journey.

长周期AI智能体接力高价值客户旅程：商业化与应用范式演进 AI智能体正从处理孤立、简短的任务转向跨越数周乃至数月的长周期复杂客户旅程，通过解决传统业务在接力环节中的价值流失，推动AI商业模式从单纯的降本转向分享业务营收增量。 长周期智能体突破了单次客服交互或简短封闭任务的局限，能够自主跟进长达数周至数月的复杂流程。 在原告律所等典型“接力赛”业务中，智能体可通过即时评估、持续取证与完整记录弥补各个交接环节的价值损耗。 拥有完整旅程让AI供应商得以探索基于结果的定价机制，但同时面临行业合规、采购认知与归因争议等挑战。 市场落地存在销售平台软件、替代外包服务以及全栈垂直整合三种不同商业化路径。 从即时响应到长周期全流程驱动 早期的客户支持AI主要处理明确边界、简短互动的任务，如总结通话或撰写跟进邮件。而如今长周期智能体能够跨越数十个步骤、多种沟通模态和长期时间线工作。这使AI能够覆盖如保险理赔、房贷审批和企业大单销售等跨越数周乃至数月的端到端复杂旅程，使AI厂商的价值不再局限于单次效率提升。 修补传统“接力赛”业务的价值漏洞 以原告律所为例，传统的办案过程如同一场存在大量损耗风险的接力赛：因评估延迟流失潜在客户、人力受限导致证人沟通与细微证据审查不足、信息跨系统分散造成关键账单被遗漏。长周期智能体可实时快速初审，按需调遣子智能体进行数十次跟进，对每项证据进行细致审查，并维持完整的案件上下文记忆，避免关键信息丢失并提升最终和解价值。 商业模式变革与基于结果的定价挑战 掌控全流程使供应商能够转向基于工作成果或最终收益的定价，从而参与客户的收入扩张。历史上的数字外包销售巨头Red Ventures曾通过全链条销售在2022年实现约14亿美元营收和3.7亿美元EBITDA，但由于消费者发现渠道变化以及AI带来的复杂流程处理能力，其商业模式正受到冲击。与此同时，结果定价在现实中面临法律限制（如律所禁止非律师分成）、企业采购缺乏先例以及初期归因争议等商业挑战。 走向市场的三种落地路径 长周期智能体目前存在三种商业化路径：一是作为平台软件出售给企业以融入现有运营，虽具有高定价空间但销售周期较长且归因复杂；二是作为替代外包服务出售，虽然预算明确且周期较短，但旧有外包定价可能限制其价值体现；三是直接垂直整合走向全栈原生模式，虽然能完全捕捉AI收益，但必须承担合规与牌照等复杂的运营负担。

Traditionally, calling your internet provider’s support hotline about an outage went something like this. A robotic voice greeted you as a valued customer, then made you spend several minutes on phone menus (“press 3 for technical support, 2 for internet, then 4 for all other issues”). You were put on hold, feeling forgotten, until a service representative finally picked up and asked for your name, account number and issue. If restarting your router didn’t fix the problem, you were put on hold again, only to tell another representative the whole story a second time before receiving a technician appointment. AI is already transforming support interactions like this one. Instead of grappling with a robotic voice and several service representatives, an AI agent can now pick up right away. It can see your account, check whether other customers in your area are also affected and either resolve simple problems directly or book a technician. No more “all our service representatives are currently busy” and no more hold music. Until recently, this was the technological limit. But now, new capabilities are allowing agents to follow threads and execute tasks over longer horizons. We believe this will allow AI companies to go beyond individual customer interactions and rethink how to address complex customer journeys that run over weeks and months. AI will guide insurance customers from an initial call to payout, mortgage borrowers from pre-approval to closing, and enterprise leads from their first demo to signature. This will not only create better outcomes for those customers, but also let the AI vendor get paid for the resulting revenue expansion, not just for cost savings. There are many kinds of businesses where creating value for customers takes many steps, over weeks and months. Let’s take the traditional plaintiff law case preparation journey as an example. Prospective cases often arrive as leads by phone, where intake specialists gather the key facts and pass them to an attorney who evaluates whether to take on a client. Once a firm signs a case, investigation and evidence collection follow: Paralegals order police reports, try to get in contact with witnesses, and send spoliation letters to defendants to preserve evidence while keeping clients updated on progress. Firms then wait for client medical treatment to conclude before drafting a medical chronology and handing information to attorneys to finalize the demand letter. This process is what we historically would have called a “relay race”: a long journey with risk of drop-off or value leakage at each handoff. Potential clients usually call several personal injury firms and often sign with the firm that gets back to them the quickest. So cases can drop out of the funnel if clients only reach voicemail or if the qualification decision is slow. Once a client is signed, evidence collection involves many parallel threads at once and firms may not have the persistence to call a potential witness for the fifth time after the previous four attempts went unanswered. In addition, labor constraints can keep firms from reviewing every piece of evidence in detail, especially for seemingly routine, low-value claims. A firm may accept the first offer when a detailed read of the doctor’s note would have surfaced indicators of a brain injury, increasing settlement value. Finally, cases can run for months, require numerous contributors, and involve information distributed across documents, stakeholders, and systems. This makes it nearly impossible to maintain the full picture on every client. As a result, important bills may not be reflected in the demand letter or treatment gaps may remain unexplained, driving lower settlement value. Until recently, AI agents mostly handled bounded tasks (“Send this follow-up email”, “Summarize that call”, “Create this document”) and narrow interactions. For instance, customer support has been a great early AI use case because the problem space is well-defined, making high reliability achievable, while still allowing for customization based on company-specific policies. Interactions are brief, outcomes are verifiable, and if AI agents encounter edge cases they cannot handle, existing support teams are usually still in place for escalations. Now, long-running agents are able to pursue goals across dozens of steps, modalities, and longer time horizons, allowing them to take on relay races from start to finish. In the plaintiff law example, a long-running agent could be assigned to a case from the initial intake call with the goal of maximizing the client’s recovery. The agent would save and retrieve context on the case as needed. It wakes based on events or deadlines, proactively dispatches sub-agents to contact witnesses, update the client, and interact with attorneys across phone, email, or text. It then consolidates findings, plans subsequent moves, drafts documents for review, and drives the case towards the best possible outcome. Across clients, it keeps learning, as each case generates data which helps the agent process the next case better than the current one. The agent addresses key value leaks observed in today’s relay race. At the start of the journey, clients are no longer lost to slow case evaluation as the agent is available regardless of the hour or weekday and can assess cases nearly instantaneously. Evidence collection is no longer bounded by human capacity, as the case agent can spin up an additional sub-agent at any moment to place the fifth, tenth, or even twentieth phone call. As agents are nearly infinitely scalable, even seemingly routine cases get worked fully and each piece of evidence is reviewed in detail, surfacing the potential brain injury that might otherwise have been missed. And finally, the agent keeps a complete record of the case, from witness phone call transcripts to police reports and medical records, making sure no information gets lost. Beyond closing these leaks, owning the full customer journey can change the economics for the agent vendor. As agents help deliver outcomes with higher quality at greater speed, they can drive revenue uplift, not just cost reduction for customers. More importantly, as impact is observable and attributable to agents, vendors can participate in the upside they create by pricing based on work products or outcomes. In plaintiff law, for instance, a single case is a discrete unit of work and the value of the resulting outcome, the settlement amount, is clearly identifiable. In addition, outcomes can be linked to agents more easily as they drive a larger share of the final work product. Vendors may then share in topline expansion, reaching contract values previously unattainable by solely focusing on cost reduction. Admittedly, switching to outcome-based pricing is not frictionless. In plaintiff law, most states prohibit non-lawyers from sharing in legal fees, so pricing would need to align with settlement values without taking a direct share of them. In other verticals, vendors may face objections from procurement teams who do not yet have precedents for outcome-based contracts. And even though agents move closer to fully owning outcomes, attribution debates will likely surface initially. Successfully deploying long-running agents will therefore be a commercial challenge as much as a technical one. Still, getting paid for outcomes on high-value customer journeys is not a new idea. Red Ventures (RV) is a great historical example. RV was founded by Ric Elias and Dan Feldstein and pioneered digital end-to-end outsourced sales operations for long-term consumer contracts. As an example, for insurance policies, RV would collect details on customers when they were searching for these products (by owning sites like homeinsurance.com), reach out, walk them through coverage options, and close the policy. By fully owning this journey, RV collected a payment from the insurer for each policy it closed. This used to work extremely well: according to S&P Global, RV generated [\~$370M in EBITDA on \~$1.4B in sales in 2022](https://www.spglobal.com/ratings/en/regulatory/article/-/view/type/HTML/id/3363913). Now, this model seems structurally under threat. At the top of the funnel, consumers are changing how they discover products. Instead of relying on Google search or comparison sites to explore and compare products, they can now ask a personal agent like Meta’s Muse or Instinct for product recommendations directly, lowering the value of owning traditional exploration sites. Further downstream, instead of hundreds of humans running linear funnels, long-running agents can work across far more complex journeys. They will do so across products, and communication modalities, in parallel, in any language and at scale, with infinite persistence. RV’s EBITDA [decline to an estimated \~$160M in 2025](https://www.spglobal.com/ratings/en/regulatory/article/-/view/type/HTML/id/3363913), driven by customer churn and softer ad spend, provides an early indication of the model’s vulnerability as AI will transform the industry further going forward. There are different ways to bring long-running agents to market. (1) **Selling the Platform:** Under this motion, the vendor sells the long-running agent as software, supporting the enterprise in its existing operations. The plaintiff law case agent integrating with an existing team and their workflows is a good example. Selling a platform allows for revenue upside as there are often no comparable software or outsourced services contracts in place anchoring prices. This, however, also extends sales cycles as new budget lines need to be created, and it is harder to clearly attribute outcomes to an agent that interfaces with several departments across the enterprise. (2) **Selling the Service:** Here, the vendor takes over an already outsourced service to deliver a desired outcome more effectively, just like Red Ventures owning the entire customer acquisition journey for an insurance carrier. This approach shortens sales cycles as the ROI math for buyers is simple and budget lines for BPOs already exist. However, existing, pre-AI pricing models may not reflect the true value the long-running agent can deliver and many high-value journeys are run in-house today, limiting the reach of this approach. (3) **Going Full-Stack**: A full-stack approach means vertically integrating and becoming an AI-native player. For instance, instead of selling a long-running agent to a debt collection firm, you could buy debt portfolios and collect them yourself, allowing you to fully capture any AI upside created and address attribution problems. However, this also means owning parts of the service like licensing or compliance, which are outside the actual customer journey, adding distractions and operational complexity. It is too early to tell which of these approaches will prevail, but we are convinced that long-running agents will win the relay race at scale and expand the market addressable by AI from a share of labor budgets to a share of revenue. Plaintiff case preparation is only one example and we expect to see agents drive outcomes across other high-value journeys like mortgage origination, insurance claims processing, complex enterprise sales and countless others. If you are working on capturing this opportunity, we would love to hear from you! *This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. 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