Launching today
Voker
The Agent Analytics Platform for AI Product Teams
155 followers
The Agent Analytics Platform for AI Product Teams
155 followers
Voker is the Agent Analytics Platform for AI product teams. It gives you the usage behavior and agent performance insights you need to monitor and optimize your production agents at scale. Install the lightweight, provider agnostic SDK and Voker handles the rest: automatic intent, correction and resolution detection on your user to agent interactions, conversation reconstructions, queryable timelines, agent performance tracking so you can build the best agents possible.
Voker
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 data
You 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/
@tyler_postle Hey Tyler — congrats on the launch 👋
The "third leg of the agent monitoring stool" framing really resonates. I'm running a few agents in production myself (Telegram + VK Teams bots fronting an OpenClaw agent), and the gap I keep hitting isn't detecting that something went wrong - it's reconstructing why. Logs show the tool calls, but the model's reasoning between turns is gone unless I instrument it manually.
Quick question: does Voker capture the reasoning/thinking blocks, or just the user-facing turns and tool I/O? That's basically the line between "agent monitoring" and "agent debugging" for me.
Either way - good to see someone taking the analytics angle seriously instead of just shipping another eval framework. Will give the SDK a spin this week.
@tyler_postle Can Voker track performance regressions after a prompt, model, or tool change, and show whether success rates dropped for specific intents?
Voker
@sead_sehovic Yes! As long as you version your agent in our SDK, we allow you to segment your data across the platform by version. If you take a look at our demo video in the launch, you can see where we have resolution rate and correction rate by intent categories by version (that's a mouthful!)
Most observability tools treat agent calls as black boxes, logging tokens but missing the decision loop entirely. Building RetainSure's AI workflows, we struggled to attribute downstream outcomes back to specific agent choices. Our logging was ad hoc and we ended up rebuilding it multiple times. Does Voker capture branching decisions when an agent picks between tool calls, or is it focused on input/output tracing?
Voker
@retain_dev Yes! Voker automatically tracks all the information your agent is provided to make its decisions, so you can see both the tools available and the tools used. This has helped our customers notice that their tools may need new descriptions when the agent has what it needs but isn't calling the right tool.
Love the brutal honesty here AI has definitely written checks that devs are stuck cashing in production. Quick question on the SDK: how does it handle semantic variations for corrections? Will it catch things like actually scratch that versus no that's wrong out of the box, or do we need to train it on our own domain vocabulary?
Voker
@vikramp7470 Good question - Voker will detect those kind of phrases, even with semantic variation. That being said, if you have super specific domain vocabulary, where two words might mean the same thing to a lay-person but not to you as a domain expert - then you will need to pass Voker some context in the form of either knowledge docs or feedback on our annotations (APIs for these are in the works!)
thanks Vikram!
@tyler_postle Makes sense semantic variation handling is honestly the hard part in production. Cool that Voker already catches most of that out of the box 👌
Voker
@vikramp7470 Thanks! I'll pass your positive feedback to the team, our founding engineer Zach spent a lot of time working on that detection system because its foundational to all the other analytics our platform provides.
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?
Voker
@dhiraj_patel5 We're actually purpose built for complex, long running, multi-intent conversations! When our SDK detects multiple intents within a conversation, they get categorized into "Session Paths" that show up in our session timeline. This way you can easily navigate to different parts of the conversation without scrolling through the whole session. You can also analyze the accuracy of the agent on these separate intents across other surfaces in our product.
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?
Voker
@natalia_iankovych When you send the information from your vector DB to your agent, Voker will also track that context. We'll use the information from your own RAG data to make our assessment on the quality of the response to the user! Essentially any information that your agent has to make its decision - Voker will also track and assess.
Voker
@lakshminath_dondeti Today most teams link the dashboards or screenshots to their coding agents to implement fixes. We're working on releasing Analytics APIs so your agents can directly query Voker, make changes to prompts/harnesses/code/tools and ship fixes on its own!
Autumn
amazing team building something that’s really needed! Congrats on the launch 💗
Voker
@ay_ush Appreciate you guys! Autumn has made billing seamless - and for a small but mighty team like ours that's a huge time saver and value add. Esp love that you're purpose built for AI products <3