AgentOps Autopilot

Pitch

AgentOps Autopilot is a reliability layer for AI agents. It watches agents as they work, captures every step they take, evaluates whether the outcome is correct, and helps teams understand why an agent failed when something goes wrong.

The product uses Vertex AI / Gemini to power the agent workflow, Arize Phoenix to trace and evaluate agent behavior, and GitLab to turn approved fixes into trackable issues or merge requests. Instead of only showing a successful agent run, AgentOps Autopilot shows the full reliability loop: run, trace, evaluate, diagnose, approve, fix, and verify.

The core idea is simple: when an AI agent fails, teams should not have to guess what happened. AgentOps Autopilot explains the failure with evidence, recommends a fix, asks for human approval, and then verifies whether the fix actually improved future runs.

What It Does

AgentOps Autopilot helps teams make AI agents safer, more reliable, and easier to improve. It records agent runs, checks the quality of each result, identifies common failure patterns, and produces a human-reviewable fix proposal.

The system does not silently change the agent on its own. It supports autonomous diagnosis but keeps remediation under human approval. Once a fix is approved, the platform can create a GitLab issue or merge request and rerun the workflow to confirm that the agent improved.

Why It Matters

AI agents are starting to perform real work across support, engineering, operations, data, and business workflows. But when they fail, it is often unclear whether the problem came from the prompt, the model, a missing tool call, stale data, bad retrieval, weak permissions, or an unsupported final answer.

AgentOps Autopilot gives teams a clear way to inspect and improve agent behavior. It turns failed runs into learning signals, failed traces into regression tests, and repeated mistakes into approved fixes.

Key Use Cases

Monitoring production AI agents
Debugging failed or low-quality agent runs
Evaluating whether agent outputs are grounded in evidence
Detecting missing tool calls or skipped workflow steps
Identifying unsafe actions that require human approval
Turning agent failures into GitLab issues or merge requests
Creating regression tests from failed agent runs
Improving prompts, tool policies, and agent workflows over time
Giving engineering teams visibility into agent reliability
Helping teams move from prototype agents to production-ready agents

How The Product Works

AgentOps Autopilot starts when a user runs an AI agent workflow powered by Vertex AI / Gemini. As the agent works, the system records the mission, tool calls, intermediate steps, retrieved evidence, final output, latency, and cost signals.

Those traces and evaluations are sent to Arize Phoenix, where the system can inspect agent behavior and understand what happened during the run. If a run fails, AgentOps Autopilot analyzes the trace and eval results to determine the likely root cause.

The platform then generates a Fix Card that explains the failure, shows the supporting evidence, recommends a change, and asks for human approval. If approved, the fix is routed into GitLab as an issue or merge request so the team can review, track, and implement it.

After approval, AgentOps Autopilot reruns the workflow and compares the results before and after the fix. This proves whether the agent actually improved.

System Architecture Overview

The attached architecture diagram shows how the major components work together.

At the center is the AgentOps Core, which coordinates the mission runner, failure miner, autopilot diagnoser, regression harness, fix generator, approval layer, and trace exporter.

The Mission Runner Agent uses Vertex AI / Gemini to execute the agent workflow. The Phoenix OTLP Exporter sends trace and evaluation data into Arize Phoenix. The Autopilot Diagnoser reads the trace and eval data to explain failures. The Fix Generator creates a human-reviewable remediation plan. The Human Approval Layer ensures that fixes are not applied silently. Once approved, the system can create a GitLab issue or merge request and rerun the workflow through the Regression Harness.

The result is a closed-loop reliability system for AI agents: every failed run can become a diagnosis, every diagnosis can become an approved fix, and every approved fix can be verified with a regression rerun.

Core Message

AgentOps Autopilot makes AI agent failures inspectable, evaluable, and fixable.

It connects Vertex AI / Gemini for agent execution, Arize Phoenix for observability and evaluation, and GitLab for human-approved remediation.

The product helps teams move from demo agents to production-ready agents by answering the most important question after every failure:

What went wrong, why did it happen, and what approved fix prevents it from happening again?