1 link tagged with all of: reliability + ai product design + llm reliability
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PostHog shares concrete lessons from shipping 10 AI products used by 100,000 monthly users, covering when to build AI, how to structure agents, and what fails in practice. The core insight: most AI failures are about reliability and context, not capability, and you need to rethink product architecture for agents rather than bolt them on.
- Most AI product failures come from poor reliability, unclear limits, and insufficient context—not lack of capability. PostHog's install wizard improved activation 2x and conversion 5x because it automated a 2-hour task, not because it was technically impressive.
- Agents need complete tool coverage and flexible abstractions to work. Forcing agents through preset tools or leaving out endpoint coverage (like missing experiment-start when experiment-create exists) makes them abandon tasks and ask users to finish manually.
- Context matters more than reasoning power. PostHog improved wizard effectiveness more by giving agents better context than by adding multi-step planning, subagents, or better models—and model upgrades bulldoze your assumptions anyway, so don't over-invest in harnesses.
- Build shared infrastructure and let product teams own agent features. PostHog's MCP server has 5x more daily users than their AI product because it's easier to maintain and lets teams integrate agents into their own products rather than using a monolithic assistant.