Making AI Agents Stateful and Fault-tolerant on Google Cloud Run

Cloud Run offers fast, cost-efficient serverless deployment but is inherently stateless, making it difficult to operate reliable agentic AI workflows that require durable multi-step execution, recovery from interruptions, long-running processing, and controlled scaling. The open-source DBOS library addresses these limitations by storing workflow and queue state in Postgres, enabling interrupted workflows to resume on new instances, configurable concurrency and rate limits, workload observability, code version management, and integration with Cloud Run revisions and rollouts. Dosu, an AI-native knowledge infrastructure provider serving more than 50,000 software projects, adopted DBOS after finding Temporal too complex for its serverless architecture, migrating its high-volume agent workflows without downtime. Using Cloud Run with instance-based billing, Postgres-backed DBOS workflows, a dedicated Cloud SQL instance, and Grafana dashboards, Dosu improved resilience, scaling, and monitoring for workloads involving bursts of requests and long-running onboarding processes.

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