Self Driving Products: The Next Step in Product Analytics

Engineering teams use PostHog AI Observability to debug and optimize LLM-powered features without managing a separate, expensive data silo. The platform captures every prompt, response, and tool call as a standard PostHog event, giving you instant visibility into model latency, errors, and token costs across providers like OpenAI and Anthropic. Because this data lives in your main product analytics suite, you can easily correlate LLM performance with real user behavior: linking high-latency responses directly to drop-offs in your conversion funnels.

Evaluations, specifically via the DeepEval framework, provide the necessary structure for systematic LLM testing. This open-source tool integrates directly into your CI/CD pipeline, acting like a specialized Pytest for AI applications. It leverages over 50 research-backed metrics—including G-Eval, RAGAS, and Hallucination checks—to score model performance on specific criteria. Developers define test cases, run the evaluation, and receive concrete metrics to prevent regressions, ensuring model reliability before deployment.

The Model Context Protocol (MCP) functions as a standardized integration layer: think of it as a USB-C port for AI applications. Developed and open-sourced by Anthropic, this protocol allows large language models (LLMs) to access real-time context and execute actions via external tools like GitHub, Jira, or proprietary databases . It uses a simple JSON-RPC interface to define tools, schemas, and endpoints, which enables AI agents to perform complex, state-changing tasks—such as creating a GitHub issue or running a test script—rather than just generating text . MCP is essential for building agentic AI systems that can autonomously pursue goals and operate within defined safety and permission boundaries .