akslp2080 5 hours ago

How is it different than Langfuse? sorry if I am off the track but Langfuse also provides some detailed tracing of agentic behavior and decisions.

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ttpost 5 hours ago | parent [-]

We get this question a lot! We work hand-in-hand with obs tools like Langfuse. Langfuse is great for debugging technical issues on individual traces like timing conditions that resulted in failed API calls.

Voker focuses on product, business and user outcomes - like what intents did the user bring to your agent that you might not expect. We're built for the whole product team, whereas Langfuse focuses on engineers specifically.

One way to think about it would be: a PM notices in Voker that a new intent category is coming up frequently and the agent isn't handling it well. The PM can dig into the data with visualizations or our conversation reconstructions. Once they confirm its a real issue worth addressing, they can link their investigation to the AI engineer - who can use Voker AND Langfuse to debug and implement a fix/improvement.

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bfeynman 3 hours ago | parent [-]

do you have experience as PMs? Looking at website, it looks like you just use llms to guess what categories are? Seems like trap for garbage in garbage out. Otherwise you would need someone technical to figure out how to setup the proper KPI monitoring things...

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ttpost 3 hours ago | parent [-]

We do! We have combined experience as PMs, ml engs, and data scientists across many verticals. We also have experience helping PMs and AI eng teams build agents across over 100 customers from our first product.

You're totally right, the analytics annotation primitives we detect (intents, corrections, resolutions) are the cornerstone to all the other analysis in our platform. It's critical that we get those right or all the data and insights in the world are useless.

LLMs are a core part of that detection, but we also do things like hierarchical classification, (https://voker.ai/blog/hierarchical-text-classification-with-...) and will eventually add in other ML methods where applicable. On top of our automated detections, we're building ways for the annotations to improve and adapt to your specific agent product, your data, and your feedback on our annotations.

Our SDK is architected to eventually accept any type of event you want to send as additional information like add to carts, or other conversion metrics that are valuable for analysis on agent performance.

You're definitely right, we don't expect a PM to instrument this all themselves - similar to web analytics or product analytics tools, the engineering team instruments and maintains the integration, and then our app makes the insights and data accessible to not just the engineer but the whole product team.

	▲	holoduke 5 minutes ago \| parent [-]
		Your response is AI. It's a bit ai sloppy as well. Sorry to say that. But as a business owner you can and should do better.