AI product teams have built sophisticated agents, but they often lack the right tools to watch how those agents perform in the wild. Without clear visibility into what users expect and whether agents deliver, teams stitch together workarounds—uploading logs to chatbots for summaries or manually scanning traces to hunt down issues. That reactive cycle leads to poor user experiences, churn, and wasted engineering hours.
Now a new platform called Voker is stepping in to change the status quo. Founded by Alex and Tyler, Voker provides agent analytics designed specifically for conversational AI products. Instead of forcing teams to parse raw logs or rely on engineers to spot-check configurations, Voker embeds a lightweight SDK into agent workflows. The system tracks three core primitives—intents, corrections, and resolutions—automatically categorizing conversations to reveal usage patterns without requiring manual review.
Why most AI agent monitoring falls short
Traditional observability tools shine at tracing individual API calls or debugging code paths, but they’re not built for the messy, unstructured data that powers agent conversations. Product analytics platforms, on the other hand, excel at tracking clicks and pageviews across interfaces, yet they ignore the nuance of conversational intents and user corrections.
Evaluations and benchmarks help teams test known failure modes, but they don’t surface unexpected trends or emerging pain points before users complain. Many teams resort to uploading logs to LLMs like ChatGPT or Claude, asking for summaries of conversational data. While this approach can surface high-level themes, it introduces inconsistency: LLMs struggle with precise math and data processing, leading to overfitting or underfitting insights across sessions.
How Voker turns raw agent data into actionable insights
Voker’s platform processes each LLM interaction in real time, automatically annotating conversations to extract user intent and any correction attempts. It then uses hierarchical text classification—powered by LLMs—to group similar intents into dynamic categories, giving teams a bird’s-eye view of recurring user needs without sifting through individual chats.
The system handles the heavy lifting of data engineering, transforming raw agent events into structured analytics primitives that teams can trust. Engineers no longer need to manually correlate logs or rely on brittle LLM summaries. Instead, they get consistent, reproducible metrics that reveal what users truly want from their agents—and whether those agents are delivering.
Built for speed and flexibility
Voker’s SDK wraps LLM calls to major providers like OpenAI, Anthropic, and Google Gemini, supporting both Python and TypeScript. The integration is purpose-built for agent products, meaning it handles the unique data formats and conversation structures that define AI-driven workflows. Teams can plug it in without rewriting their entire stack.
Pricing starts at $80 per month for paid plans, with a 30-day free trial and a free tier offering 2,000 events per month. No credit card is required to get started, and teams can sign up with just an email address.
The road ahead for agent observability
As AI agents move from prototypes to production systems, the need for purpose-built analytics will only grow. Tools like Voker address the gap between generic observability and specialized agent monitoring, giving product teams the visibility they need to iterate quickly and deliver reliable experiences.
The team behind Voker is actively seeking feedback from early users. If you’re currently tracking agent performance with workarounds or ad-hoc tools, try the platform and share what insights resonate—or what’s still missing. The future of agent analytics depends on tools that combine precision with ease of use.
AI summary
AI ajanlarınızın kullanıcı deneyimini iyileştirmek için Voker’in anında izleme ve analiz çözümlerini keşfedin. Ücretsiz katman ve kolay SDK entegrasyonu.
