Automate synthetic monitoring with AI workflows

Automating synthetic monitoring with AI workflows is a proactive approach to identifying and addressing system degradations before they affect users, as highlighted by Catchpoint's 2024 SRE Report, which revealed that 58% of outages are first detected by users. Synthetic monitoring simulates user interactions to establish performance baselines and detect deviations, thereby filling the gap left by real-user monitoring, which often misses gradual performance declines. CodeWords facilitates this process by allowing the creation of conversational workflows that define tests, schedules, and alert routes without needing a dedicated monitoring platform. Its AI-powered system not only detects anomalies but also provides hypotheses for potential root causes, enhancing the efficiency of incident response. The platform emphasizes monitoring critical flows such as homepage loading, authentication processes, core transactions, and key API endpoints, all while minimizing alert fatigue through strategic configurations like retry logic and severity tiers. Overall, this approach aims to equip teams with timely insights and actionable hypotheses, ensuring system reliability and performance are maintained without relying solely on user reports.

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