Why we built Agent Opz

In 2025, teams started shipping AI agents that actually do things in production — file support tickets, process invoices, send emails, make API calls. These are not demos. They are production systems with real consequences when they fail.

But the tooling never caught up. Engineers who spent years building reliable microservices suddenly found themselves deploying LLM-powered agents with no uptime checks, no cost guards, no deployment safety net, and no incident playbook. The only way they found out something was broken was when a user complained.

We built Agent Opz because we lived that problem. We believe that every primitive that exists for operating production software — monitoring, alerting, deployment, rollback — needs to exist specifically for AI agents. Agents are different from APIs. They branch. They retry. They accumulate cost. They need purpose-built ops tooling, not a general-purpose wrapper.

What we believe

• Agents are infrastructure. They need to be operated like infrastructure — with uptime monitoring, alerting, and deployment controls.
• Cost is a first-class signal. Token spend is just the beginning. Real cost accounting for agent runs includes retries, tools, and downstream effects.
• Deployment is a reliability problem. Pushing a new prompt without a canary strategy is the same as deploying untested code straight to production.
• Framework-agnostic by design. You should not have to change your agent framework to get production-grade ops. Agent Opz wraps what you already have.