Monitor, manage, and automate AI workflows (opens in new tab)

The GitLab Duo Agent Platform’s Automate capabilities provide a centralized framework for managing, executing, and monitoring AI-driven development workflows within the software development lifecycle. By integrating event-driven triggers and detailed session logging, the platform allows developers to transition from manual AI interactions to fully autonomous, production-ready processes. This orchestration layer ensures that AI agents are not only performant but also transparent and easy to audit across projects.

Resource Management for Agents and Flows

The Automate hub serves as the control center for organizing AI resources, distinguishing between agents (entities that perform tasks) and flows (structured sequences of actions).

• Resources are categorized into "Enabled" (those available for project use) and "Managed" (those created and owned specifically by the project).
• Custom agents and flows must be enabled at the top-level group before they can be activated for specific projects.
• Users can expand their automation library by browsing and enabling pre-configured resources from the GitLab AI Catalog.

Event-Driven Automation with Triggers

Triggers allow AI agents to respond automatically to specific actions within the GitLab interface, eliminating the need for manual invocation.

• Automation can be initiated through three primary event types: user mentions (e.g., @agent-name), issue/MR assignments, or reviewer assignments.
• When a trigger is activated, the system identifies the associated flow, executes the agent, and posts the final results directly back to the relevant issue or merge request.
• Common use cases include using the /assign quick action to trigger a CI/CD optimizer or a code explanation agent.

Workflow Monitoring and Session Transparency

The Sessions interface provides a detailed audit trail for every execution, offering visibility into the "black box" of AI decision-making.

• The Activity tab tracks step-by-step reasoning, showing exactly which tools the agent used and the results of individual actions.
• Execution statuses are monitored in real-time, with labels such as Running, Finished, Failed, or Input Required.
• The Details tab provides deep technical context by linking directly to Runner job logs, including system messages and full tool invocation outputs.

Practical Conclusion

To maximize the utility of the GitLab Duo Agent Platform, teams should move beyond experimental chat prompts and begin configuring triggers for repetitive tasks like code review assignments or issue triaging. Utilizing the Sessions tool is recommended during the initial rollout phase to verify agent reasoning and ensure that custom flows are interacting correctly with project data before full-scale deployment.