3 Months of AI Agents in Production: A Field Report

Purple_Network3016 · reddit · 2026-07-11

After running AI agents continuously across 3 SaaS products for 3 months, the author outlines clear boundaries for what actually works and what breaks. ### Stable Production Use Cases - **GitHub MCP**: PR triage and code review, saving ~8–10 hours per week - **Postgres MCP**: Read-only queries, handling ~30 support tickets weekly - **Playwright MCP**: QA for critical flows, recently catching 4 regressions before deployment - **Context7**: Real-time documentation, reducing library API hallucinations - **PostFast + Metricool**: Multi-platform posting and analytics, saving another ~5 hours weekly ### Major Issues - Long tasks (>15 minutes) remain unstable, frequently losing context or hitting rate limits - Browser tasks requiring login states are fragile (e.g., LinkedIn scraping breaks easily) - Costs fluctuate wildly; the author once pushed the Claude API bill to **$340** in a single week - Multi-tool orchestration still suffers from agents choosing the wrong tool - TikTok posting struggles across multiple scheduling tools due to API limitations ### Security Warnings - Emphasizes that **prompt injection** is a major risk - Cites audit findings: many public MCP servers lack authentication, and OAuth coverage is low - Recommends prioritizing official/vendor-maintained MCP servers over unknown sources ### Costs - Optimized monthly spend is around **$430**. The author considers this cheaper than a part-time hire, but notes it still requires human oversight, making it more of an "assistant" than a "replacement."

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