- Specialization
Language Modelsand 1 more specialization valuesLess
- Speech
- Business
Agent Toolingand 2 more business valuesLess
- Model Evaluation
- Software Applications
- Market
- Software Development
- Ecosystem
- Agents
- Customers
Enterprisesand 2 more customers valuesLess
- Developers
- Small Businesses
More details
- Organization type
- Commercial Company
- Ecosystem
- Agents
- Business
- Agent Tooling, Model Evaluation, Software Applications
- Specialization
- Language Models, Speech
- Market
- Software Development
- Customers
- Enterprises, Developers, Small Businesses
- Delivery models
- Hosted Software
- Revenue models
- Subscription, Usage Based
- Listing status
- Not classified
- Latest equity round
- Not classified
- Reported funding amount
- Not classified
About
Agnost AI builds product analytics and production-monitoring software for teams developing chat and voice AI agents. Its platform analyzes real conversations to identify user friction, failures, conversion problems, and missed intents, then turns high-impact patterns into reviewed agent improvements.
Market
Agnost AI competes in the LLM and AI-agent observability, evaluation, monitoring, and product-analytics market. Its positioning is broader than trace monitoring: it mines real production conversations for user intent, frustration, churn, and failure patterns, then converts those findings into evals and reviewed fixes. Its main differentiation is a production-feedback-to-improvement loop that is OpenTelemetry-native and works across LLMs and frameworks, whereas competitors commonly emphasize framework-specific tracing, open-source observability, API-proxy monitoring, or eval-driven development.
Agnost AI targets software teams building and operating production chat or voice AI agents, especially product, engineering, and AI-platform teams that need to understand user intent, failures, frustration, and churn. Its packaging spans small teams and growing organizations through enterprise teams running agents at scale.
At a Glance
Problem
Teams deploying chat and voice AI agents often cannot see why users get stuck, retry, abandon conversations, or fail to convert. Conventional evaluations test predefined scenarios, but they can miss failures that emerge in live production, making it difficult to prioritize fixes and exposing companies to lost conversions, higher support costs, and degraded customer experiences. The central use case is turning large volumes of real conversations into a clear view of where an agent is failing and which problems have the greatest business impact.
Product / Service
Agnost AI is an intelligence and product-analytics layer for conversational agents. It continuously analyzes production conversations from chat and voice systems, extracts intent signals, identifies recurring failure patterns such as retries and drop-offs, and surfaces issues that standard agent evaluations miss. The company describes the workflow as turning the highest-impact patterns into reviewed fixes, while other company material describes improvements being shipped autonomously so agents become better with each interaction.
The benefit is a closed feedback loop between live user behavior and agent improvement: teams can understand what users want, diagnose where conversations break down, and focus engineering or operations effort on the fixes most likely to improve outcomes. In effect, Agnost is positioned as infrastructure for self-improving agents rather than merely a dashboard for conversation metrics.
Market
Agnost AI competes in the emerging market for product analytics, observability, and improvement infrastructure for conversational AI agents. Its positioning spans data infrastructure and analytics for teams building chat and voice agents, with a particular emphasis on production learning and self-improvement. The available research does not identify named competitors; the most relevant competitive alternatives are therefore likely to include conventional product-analytics tools, agent evaluation and observability platforms, and internally built systems for reviewing conversation failures.
The company appears early-stage but has meaningful startup and financing signals. It was founded in 2025 by Parth Ajmera and Shubham Palriwala, is listed as YC S26 with two employees, and says it is backed by Entrepreneurs First; PitchBook reports $500,000 raised from investors including Y Combinator and Transpose Platform Management. A founder profile says the company is working with Fortune 50 companies and fast-growing startups, although the available sources do not provide verified revenue, customer counts, or a consistent revenue figure, so its commercial traction should be treated as emerging rather than fully established.
Founders & Leadership
Funding History
Transpose Platform Management
Y Combinator
Recent News
Agnost AI introduced an offering that uses an AI agent’s production traces to train a model specialized for that workload. The launch describes the model as performing better, responding faster, and costing less.
Y Combinator profiled Agnost AI as a company helping teams understand what users want, where they get stuck, and why they drop off when interacting with chat and voice agents.
WorkOS covered Agnost AI’s MCP analytics platform, which tracks tool invocations, latency, errors, and user journeys across AI clients. The article also documented a lightweight Python integration using a wrapper around an existing MCP server.
Active Roles
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Get notified when they postBusiness Model
Agnost AI uses a tiered SaaS subscription model with a free entry tier and paid monthly plans. Pricing varies by included usage or events, with higher-scale and enterprise options available.