Agnost AI is an observability and improvement layer for teams shipping LLM-powered agents in production. It extracts intent signals from every conversation, runs evals continuously, and ships improvements to your agents autonomously , so issues that would normally show up as silent drop-off (a user asking for something the agent fumbled, then never coming back) become structured signals that feed back into the agent. The pitch in the launch tweet is blunt about this dynamic. With AI agents, users rarely file tickets, they just stop using the product, which makes capturing raw user intent the missing input most teams lack today. The product is positioned at the eval and agent-tuning layer of the stack, sitting between the agent runtime and whatever model or framework a team is already using. Agnost AI works with any LLM, any framework, with a 2-minute setup, and is OpenTelemetry native , which matters for teams who have already instrumented traces and want to layer intent extraction and continuous evaluation on top rather than swap toolchains. It captures every signal, runs evals continuously, and ships improvements without manual intervention , which is the part that distinguishes it from passive observability tools that surface problems but leave the fix to engineers. The company is led by co-founder and CEO Shubham Palriwala, headquartered in Delaware with engineering and operational presence in Bangalore and San Francisco , and is backed by Entrepreneurs First . Palriwala was previously a founding engineer at Formbricks and worked on analytics at Cisco , a background in instrumentation and product telemetry that lines up directly with what Agnost is building. The launch is timely because most teams now have agents in front of users but no closed loop between user behavior and agent behavior, and Agnost is making a specific bet that the loop should be automated rather than left to weekly prompt reviews.