AO

AO is agent infrastructure for deploying AI agents from local development to production. It is presented for developers who want to avoid rebuilding retries, state management, queues, monitoring, and scaling infrastructure when an agent moves beyond a local environment. The product says it works with LangChain, LangGraph, and CrewAI agents, with more frameworks coming soon.

Its documented capabilities include built-in retries with exponential backoff, circuit breakers, and dead-letter queues. AO also provides state persistence so runs can resume from failures, along with a full audit trail of agent decisions and actions. The platform supports scaling from a single run to thousands of concurrent runs, with worker pools that adjust automatically to load. It adds isolated execution for each agent, encryption of API keys and credentials at rest and in transit, live logs, full observability, tracking of every run, tool call, token spent, and decision made, and version control with instant rollbacks and comparison across versions. Scheduling is built in as well, using cron-like timing for specific times or intervals.

Deployment is described as possible from a project directory with ao deploy or directly through GitHub via the dashboard. The page says most agents deploy in under five minutes, and typical Python agents are ready in 2-3 minutes. AO offers a free starting tier and a pricing page, and the page footer links to terms of service and privacy policy. Support is listed at support@aodeploy.com.