Voker

Hey Tyler, went through Voker's site and the "Amplitude for agents" framing is honestly the cleanest take I've read on this gap. one thing I wanted to ask, how do you detect a "correction" automatically, is it sentiment delta on the next user message or something pattern-based? that label seems to do a lot of work in the product.

Do you also handle multi-agent, multi-turn orchestrations ?

Congrats on the launch! How does Voker handle intent attribution when the agent proactively redirects the user, say, a billing agent that detects the user is actually in the wrong product area and routes them elsewhere? The intent the user arrived with and the intent the agent resolved can diverge legitimately, and in those cases it's not clear whether that should register as a correction event or a successful resolution. Curious how the analytics model handles that distinction, since getting it wrong would skew correction rates significantly for agents designed to reroute.

Prompting Vibes definitely don't scale when agents start failing silently in production. Being able to catch tool errors before a client screams at us is a huge lifesaver. great job @tyler_postle

Really cool that we can get an idea what people are using our agent for. The downside of having a powerful agent is that you don't always understand what people use it for and where it is not meeting expectations.

Oh this looks really interesting. How much of the setup is out of the box vs customizable?

How do you determine the quality of answers? I have an AI service with its own vector database. For almost any user question, we know the answer, provide tourist attractions, and we have more of them than ChatGPT. Will you be able to understand whether these are top-tier attractions or not?

Automatic intent and resolution detection is the right abstraction. Most agent monitoring tools just log tokens or latency, but you actually need to know if the user got what they came for. We're building AI-driven customer success at RetainSure and agent quality drift between deployments is a real headache. How does Voker handle cases where the user's intent shifts mid-conversation?

What’s the feedback cycle? Can we launch other agents to fix issues?

I’m Tyler - CoFounder of Voker, and I’m so tired of being disappointed by AI hype claims. I bet you are too.I studied physics in college, and worked in data science, ML, and analytics until founding Voker. I’m a skeptical person by nature (I think it's the scientist in me) and my gut reaction to any technology hype is to be cautiously optimistic until I see things proven out in data. I felt this way about LLMs when they first hit mainstream. I knew they had real potential applications, but was also worried about the lofty marketing buzz they were getting. AI as an industry has written checks that individual builders are left to cash. Promising full automation, PhD-level intelligence, and perfect results. As someone who's skeptical of that narrative, I still believe agents can genuinely deliver, but only if teams are rigorous about measuring performance in production. Every website or product has Amplitude or PostHog for click and pageview analytics; a standard way to understand who's using it and how. Agents have no equivalent, so we built Voker.We are the Agent Analytics Platform where you can:- Monitor your agents- Measure their performance- See what users are asking- Know for certain agents are delivering for your users- Optimize based on real dataYou install our SDK, and Voker collects your agent conversation data, automatically detecting:- User intents (Book me a hotel in Vegas for next Saturday with a poolside view)- Corrections (No, that room doesn’t have a poolside view!! TRY AGAIN)- Agent resolutions (Tool Result: Room Booked... Success!)These automated annotations are the foundation for building a holistic view of agent performance and user behavior in one analytics platform.We asked 100+ AI founders, product managers, and agent engineers how they monitor their agents in production and the answer was resounding: by combing through individual traces (with the occasional evals sprinkled in). They all reported that they depend on customer complaints to tell them when agents are messing up. We feel strongly that there is a third leg of the agent monitoring stool missing - Agent Analytics. You shouldn’t have to wait for users to complain to learn that a recent prompt change is breaking your hotel booking agent, or that the AI finance advisor you built is calling the wrong tool to look up realtime stock prices.Turns out the antidote to AI hype is simple: measure your agents diligently, then iterate until you get it right.Your users deserve better AI experiences (we all do)! Install the Voker SDK on our free tier (up to 2,000 events/mo), and start building better agents today: https://voker.ai/