Framework
AgentOps
An operational discipline for running AI agents in production — covering observability, incident response, and the feedback loops that prevent silent drift.
The problem
Most organisations treat AI agent deployments like software releases. They are not. Software fails loudly; agents fail quietly. A misconfigured agent can spend weeks producing plausible-looking output that is subtly wrong, and the organisation only discovers it when a downstream decision compounds the error. Traditional DevOps tooling does not catch this class of failure.
The framework
AgentOps is the operational discipline for running agents reliably. It has three components: structured observability (logging not just outputs but reasoning traces and tool calls), boundary testing (the scheduled adversarial queries that probe for drift before it affects production), and a tiered incident-response playbook that distinguishes output quality degradation from safety boundary violations.
When to use it
Apply AgentOps when moving an AI agent from pilot to production, when an agent is exhibiting inconsistent output quality, or when building the internal operating model for an AI platform team. The framework is especially valuable for organisations with multiple agents in production — the discipline compounds across a portfolio.
What success looks like
An organisation running AgentOps has a mean-time-to-detection for agent quality degradation measured in hours, not weeks. Every agent in production has a named owner, a monitoring dashboard, and a clear escalation path. The platform team can distinguish "the model got worse" from "the world changed" — and respond differently to each.