Launch HN: Voker (YC S24) – Analytics for AI Agents
新品发布：值得关注的里程碑
Hey HN, we're Alex and Tyler, co-founders of Voker.ai (https://voker.ai/), an agent analytics platform for AI product teams. Voker gives full visibility into what users are asking of your agents, and whether your agents are delivering, without having to dig through logs. Our main product is a lightweight SDK that is LLM stack agnostic and purpose-built for agent products. (https://app.voker.ai/docs)Agent Engineers and AI product teams don’t have the right level of visibility into agent performance in production, which results in bad user experiences, churn, and hundreds of hours wasted with spot checks to find and debug issues with agent configurations.Demo: https://www.tella.tv/video/vid_cmoukcsk1000i07jgb4j65u67/vie...We recently conducted a survey of YC Founders and 90%+ of respondents said that the only way they know if their Agents are failing users in production is by hearing complaints from customers. They push a prompt change hoping that it fixes the problem and doesn’t break something somewhere else, and the cycle repeats.We saw tons of observability and evals products popping up to try to address these problems, but we still felt like something was missing in the agent monitoring stack. Obs is good for individual trace debugging but is only accessible to engineers. Evals are good for testing known issues, but don't give insights into trends that teams don’t expect, so engineers are always playing catch up. Traditional product analytics tools do a good job tracking clicks and pageviews across your product surface but weren’t built ground up for agent products. Knowing what users want out of agents, and whether the agent delivered requires specific conversational intelligence / unstructured data processing techniques.We came up with the agent analytics primitives of Intents, Corrections, and Resolutions to describe something pretty much all conversational agents had in common: a user will always come to an agent with an intent, the user might have to correct this agent on the way to getting their intent resolved, and hopefully every intent a user has is eventually resolved by the agent. Voker processes LLM calls by automatically annotating individual conversations and picking out user intent and corrections. Voker takes these and uses LLMs and hierarchical text classification to create dynamic categories that give higher level insights so you don’t have to read individual conversations to know what are the main usage patterns across your users.The most common substitute solution we’ve seen is uploading obs logs to Claude or ChatGPT and asking for summary insights. There are a few problems with this - mainly that LLMs aren’t good at math or data science, so you don’t get accurate or consistent statistics. Its highly likely that the LLM overfits to some insights and underfits to others. The LLM isn’t programmatically reading and classifying each individual session or interaction. This is why we don’t use LLMs for any of our core data engine
产品亮点
Voker is the Agent Analytics Platform for monitoring and improving your AI agents. Companies like Dutch.com use our SDK to build better agents. Alex and Tyler met at a high-growth E-Commerce startup w
The Agent Analytics Platform for monitoring and improving your AI agents in the wild. No more digging through logs, fix agents before users complain.
Black Forest Labs isn't the only AI innovation making waves—Voker, a startup from YC S24, debuted their analytics platform for AI agents this week, quickly gaining traction on Hacker News with a
发布意味着什么
每次重要产品发布，都代表行业技术或市场的一个新基准。它不仅影响发布方自身的竞争地位，也会倒逼整个行业加速迭代。对用户来说，新产品通常意味着更好的体验和更多的选择。
值得注意的细节
发布会的重点往往不全在主产品上，一些看似小的功能更新或合作宣布，往往是更大变化的信号。建议关注：发布现场的演示案例、合作伙伴的出席、以及发布后的社区反馈。
总结
这款产品的发布是2026年科技行业的一个重要节点。建议持续关注其正式上市后的用户反馈和市场份额变化。