This is AgentOps Commander, a human-supervised incident response agent for real-world operations teams.
Most agents can answer questions. This one is designed as a Gemini and Google Cloud Agent Builder workflow: it plans, investigates, asks for approval, acts, and reviews its Arize Phoenix traces.
The current incident is a World Cup merchandise launch. Failed orders jumped from 1.8 percent to 11.6 percent in 22 minutes.
Customers with international shipping addresses are stuck at checkout. The goal is to find the likely cause and propose a safe mitigation before the promo window closes.
I run the agent. It creates a plan with an explicit safety boundary, then calls tools to compare order metrics, inspect deployments, search logs, and review Phoenix-style traces.
The key point is that the agent is not guessing. It builds an evidence path before recommending action.
The agent finds that failures are concentrated in cross-border express shipping orders and links the spike to a configuration change that lowered the shipping quote timeout from 6 seconds to 900 milliseconds.
It proposes a mitigation: restore the safer timeout, enable cached fallback quotes, and notify support with affected order IDs.
The action changes operational state, so the agent cannot execute it by itself. I approve the action.
The safety gate changes from pending to approved, and the execution result is recorded.
Now I switch to the trace view. Each tool call is represented as a span with latency, token use, and an evaluation result.
Then I switch to evaluations. The agent is scored for task success, evidence coverage, action safety, and self-improvement.
The self-review loop is the differentiator. Using Arize Phoenix and Phoenix MCP, the agent can review weak traces and improve its next run.
AgentOps Commander is an observable, evaluated, human-supervised agent that can safely get real work done.