On the npm registry, a small package called agnost ticks over to version 0.1.10, last published about a month ago, with nine other projects already pulling it in [npm]. It is a thin SDK with an outsized ambition: give the people building Model Context Protocol servers and AI agents the same kind of product analytics that web teams have taken for granted since Mixpanel.
That is the bet behind Agnost AI, a San Francisco pre-seed founded in 2024 by Shubham Palriwala and Shubham Patel, and backed by Entrepreneurs First and Transpose Platform. The pitch on the homepage is direct: "Product analytics for AI agents. See which intents convert, where users drop off, and what's driving retention" [Agnost AI].
The bet
Agnost sells a conversation-level analytics layer for AI-native products, with a Python SDK for instrumenting agent interactions and native hooks into MCP servers and Claude Code plugins [Agnost AI Docs]. The company tracks tool invocations, task execution, performance, and what it calls satisfaction signals [Agnost AI Blog]. On top of that telemetry sits an Agent Experience Score, a single composite number meant to function the way a North Star metric does for a consumer app [Agnost AI Blog].
The wedge is MCP. The protocol, originally pushed by Anthropic, has become a default way for agents to call external tools, and operators of those servers currently have very little visibility into what models actually do once a tool is exposed. Agnost's blog leans hard into that gap, with technical posts on long-running task patterns under SEP-1686, plugin usage tracking, and how to lift adoption of an MCP server [Agnost AI Blog].
Why it could be big
Product analytics has historically been a category that compounds. Once an instrumentation SDK is wired into a codebase, it tends to stay there, and the vendor that owns the event schema tends to own adjacent workflows: dashboards, alerting, experimentation, eventually billing attribution. Agnost is trying to claim that position for a generation of products where the unit of interaction is a conversation rather than a pageview.
The investor signal is consistent with that thesis. Entrepreneurs First is a talent investor that typically writes the first check into technical founders before there is much product, and Transpose Platform has backed early infrastructure bets in the AI tooling stack. The bet they are underwriting is that conversation analytics for agents becomes its own category, distinct from LLM observability tools focused on prompts, traces, and evaluations.
The early benchmarks Agnost has published hint at where the company wants to plant a flag. In a post defining the Agent Experience Score, the team writes that customer support agents, the most mature category, see good products cluster between 72 and 80, with intent-routed deployments in the top quartile [Agnost AI Blog].
| Metric | Value |
|---|---|
| Good support agent score (low) | 72 score |
| Good support agent score (high) | 80 score |
| Max possible score | 100 score |
The team and traction
Palriwala is cofounder and CEO, confirmed in a recent podcast appearance discussing MCP server metrics [YouTube]. He holds a Bachelor of Technology in Computer Science from Vellore Institute of Technology, graduating in 2023, with prior engineering stints at Onboarding.club, Formbricks, Cisco, and Google Season of Docs [RocketReach]. Patel is the second cofounder [LinkedIn].
Public traction signals are still modest and consistent with a pre-seed timeline: an SDK shipping monthly point releases, nine downstream packages on npm, a steady cadence of long-form technical blog posts, and two recorded podcast conversations including a joint Q&A with Shinzo Labs' Austin Born on MCP analytics [YouTube].
The honest counterfactual
The bear case is the crowd. LLM observability is already contested by venues like LangSmith, Langfuse, Arize, and Helicone, and general product analytics incumbents are extending into AI use cases. A new entrant has to convince buyers that conversation-level analytics for agents is a separate purchase from prompt-level tracing, not a feature of it. Agnost's answer is that MCP and agent workflows produce a different event shape: tools, intents, multi-turn satisfaction, plugin usage, long-running tasks [Agnost AI Blog].
What to watch
The next twelve months should answer two questions. First, does Agnost convert SDK installs into paying design partners with named logos. Second, does the Agent Experience Score get cited by anyone other than Agnost, the leading indicator that a vendor is starting to set category vocabulary rather than just publish into it.