The hardest part of building an AI agent isn't getting it to work - it's knowing when it isn't.
Voker, a Y Combinator Summer 2024 company founded by Alex and Tyler, is launching an analytics platform built specifically for teams shipping AI agent products. The core product is a lightweight SDK that plugs into whatever AI stack you're already using - it doesn't care whether you're running OpenAI, Anthropic, or something else - and surfaces a dashboard showing what users are actually asking your agents, and whether those agents are successfully resolving requests.
The gap Voker is addressing is real. Most monitoring tools were built for traditional software, where you track things like error rates and server response times. Agents have a different failure mode: they respond, but the response is wrong, unhelpful, or beside the point. A 200ms response time means nothing if the agent answered a billing question with a boilerplate FAQ.
What the SDK Tracks
Voker's SDK is LLM-agnostic, meaning it works with any language model rather than locking you into one provider. The dashboard surfaces conversation-level data - what users asked, how agents responded, and whether the interaction actually resolved anything. The goal is to replace what most AI teams do today: dig through raw server logs to understand why something went wrong.
The target users are agent engineers and AI product managers - people responsible for products where an AI handles real user interactions at volume. As more companies ship AI into customer-facing workflows, the gap between "the agent gave an answer" and "the agent gave a useful answer" stops being a technical footnote and starts affecting retention metrics.
Voker is entering a competitive space. Tools like Langfuse and Arize are also working on AI observability - the practice of monitoring what AI systems are doing in production - but most evolved from the model training world and adapted toward agents. A team building agent-first from launch has a different set of instincts. Whether the SDK integrates cleanly with major agent frameworks like LangChain, LlamaIndex, and CrewAI is the practical question that determines adoption. Documentation is at voker.ai/docs.