{"id":62416,"date":"2026-03-26T22:19:07","date_gmt":"2026-03-26T19:19:07","guid":{"rendered":"https:\/\/saudishopper.com.sa\/en\/building-ai-agents-lessons-posthog\/"},"modified":"2026-03-26T22:19:09","modified_gmt":"2026-03-26T19:19:09","slug":"building-ai-agents-lessons-posthog","status":"publish","type":"post","link":"https:\/\/saudishopper.com.sa\/en\/building-ai-agents-lessons-posthog\/","title":{"rendered":"Building AI Agents: Five Lessons PostHog Learned Over Two Years"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Building AI agents is a challenge that PostHog spent two years navigating, releasing its first &#8220;AI product assistant&#8221; six months after starting and relaunching it as PostHog AI in November 2025. Ian Vanagas, a team member at PostHog, documented five key lessons the company wished it had known from the start.<\/p><h2 class=\"wp-block-heading\">Should You Build an MCP Server Instead?<\/h2><p class=\"wp-block-paragraph\">Before committing to a custom <a href=\"\/category\/ai\/\">artificial intelligence<\/a> agent, PostHog suggests evaluating whether a Model Context Protocol (MCP) server would suffice. The company said 34% of dashboards created by AI were generated through its MCP server, accounting for 18% of all dashboards created. MCP servers are simpler to build and require less maintenance, making them suitable when users are developers or when validating demand for agent capabilities.<\/p><h2 class=\"wp-block-heading\">The Agent Harness Is Not a Competitive Advantage<\/h2><p class=\"wp-block-paragraph\">PostHog iterated through three harness architectures before settling on the Claude Agent SDK with MCP tools. The first used a coordinator routing messages to sub-agents, which caused context loss. The second used a single agent loop with 44 tools, which did not scale. The third and current version uses a code-execution sandbox, giving the agent flexibility without requiring the team to build every capability manually.<\/p><h2 class=\"wp-block-heading\">Building AI Agents Around Context Creates a Real Advantage<\/h2><p class=\"wp-block-paragraph\">PostHog said context is the most important differentiator when building AI agents. The company structures its context through MCP tools, markdown skill files, layered runtime context injection, a taxonomy tool, and a memory onboarding flow. These components allow the agent to understand both the product and individual user goals over time.<\/p><blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><p>&#8220;Reality has a surprising amount of detail and people define tasks in ambiguous ways.&#8221;<\/p><cite>Ian Vanagas, PostHog<\/cite><\/blockquote><h2 class=\"wp-block-heading\">Observability and Evaluation Must Start on Day One<\/h2><p class=\"wp-block-paragraph\">PostHog said it regretted not setting up <a href=\"\/category\/apps\/\">observability<\/a> tools early. The company now recommends tracing every LLM call with inputs, outputs, latency, and cost, along with trace IDs spanning full conversations and automated scorers. The team also runs a regular &#8220;traces hour&#8221; session where members manually review real user interactions to identify areas for improvement.<\/p><h2 class=\"wp-block-heading\">User Experience Matters More Than Capabilities<\/h2><p class=\"wp-block-paragraph\">PostHog found that the most common user pain points were not related to the range of <a href=\"\/category\/ai\/\">AI<\/a> capabilities. Instead, users reported inconsistent performance, unexpected failures, generic error messages, unclear capabilities, and a lack of visible uncertainty or progress indicators. The company said addressing these issues proved more valuable than adding new features.<\/p><p class=\"wp-block-paragraph\">Vanagas stated that building an AI agent is ultimately a product engineering problem, not a technical showcase. The company said teams should talk to users, ship features users want, and iterate continuously to build an agent that solves real problems. PostHog&#8217;s experience over two years shows that <a href=\"\/category\/ai\/\">building AI agents<\/a> requires as much focus on product fundamentals as on model capabilities.<\/p>","protected":false},"excerpt":{"rendered":"<p>PostHog&#8217;s Ian Vanagas shares five lessons from two years of building AI agents, covering architecture decisions, context design, observability, and user experience priorities.<\/p>\n","protected":false},"author":10,"featured_media":62415,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_meta-robots-noindex":"","_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_pr_autopilot_social_copy":"{\"x_hook\":\"PostHog spent two years building AI agents, went through 3 architectures, and built 44 tools before finding what works. Here are the 5 lessons they wish they knew from day one.\",\"x_takeaways\":\"Key Highlights:\\n\ud83d\udd39 34% of PostHog's AI-created dashboards came from its MCP server \u2014 often simpler than a custom agent\\n\ud83d\udd39 Context is the real differentiator: skills, runtime injection, and memory flows beat raw model capability\\n\ud83d\udd39 The biggest user complaints were inconsistent performance and unclear errors \u2014 not missing features\"}","_yoast_wpseo_opengraph_title":"Building AI Agents: Five Lessons PostHog Learned Over Two Years","_yoast_wpseo_opengraph_description":"PostHog spent two years building AI agents and relaunched as PostHog AI in November 2025. The company shares five lessons on architecture, context, observability, and what users actually want.","_yoast_wpseo_opengraph_image":"https:\/\/saudishopper.com.sa\/en\/wp-content\/uploads\/2026\/03\/building-ai-agents-posthog-lessons.webp","_yoast_wpseo_opengraph_image_id":62415,"_yoast_wpseo_twitter_title":"PostHog's 5 Hard-Learned Lessons on Building AI Agents","_yoast_wpseo_twitter_description":"PostHog iterated through 3 agent architectures and 44 tools before finding what works. Here are the 5 lessons they wish they knew from day one.","_yoast_wpseo_twitter_image":"https:\/\/saudishopper.com.sa\/en\/wp-content\/uploads\/2026\/03\/building-ai-agents-posthog-lessons.webp","_yoast_wpseo_twitter_image_id":62415,"_yoast_wpseo_primary_category":449,"_extra_hide_author_box":"","_extra_featured_post":"","_partner_post_id":84211,"footnotes":""},"categories":[442,449],"tags":[864,1776,2913,2912,2914],"class_list":["post-62416","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-apps","category-ai","tag-ai-agents","tag-fulfillment-center","tag-mcp-server","tag-posthog","tag-product-engineering","et-has-post-format-content","et_post_format-et-post-format-standard"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Building AI Agents - PostHog&#039;s 5 Lessons | Saudi Shopper<\/title>\n<meta name=\"description\" content=\"Building AI agents took PostHog two years and three architecture iterations. 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