How Sherlocks AI uses Temporal to orchestrate AI agents for incident resolution

This post details how Sherlocks AI leverages Temporal's Durable Execution for orchestrating AI agents in incident resolution. It describes four distinct workflow patterns: Knowledge Graph construction (replacing cron jobs with scheduled Workflows), Infrastructure scanning (handling long-running tasks with retries and checkpointing), AI agent investigations (managing complex multi-stage processes with specialized agents), and Event ingestion (ensuring durable acknowledgment of triggers). The post highlights the benefits of Temporal's retry policies, scheduling, human-in-the-loop capabilities via Signals, and observability for these patterns. It also emphasizes Temporal's model-agnostic approach, allowing flexible use of different LLM providers and models within Activities. Structural choices include per-tenant Workflows for isolation, scaling Workers as separate processes, and using a Codec Server for data encryption.