Incident management in distributed environments has become one of the biggest challenges for operations teams. When a failure occurs at 3 AM, the on-call engineer must navigate dozens of microservices, scattered logs, traces stored across multiple locations, and an arsenal of observability tools. The cognitive overload is enormous, and any delay in diagnosis can translate into financial and reputational losses. Faced with this scenario, the combination of artificial intelligence and automation offers a real solution: building an autonomous SRE agent capable of orchestrating the entire diagnosis and post-mortem process without human intervention. In this article we explore how to implement such a system using Google Agent Development Kit (ADK) and Antigravity SDK, two technologies that enable creating intelligent agents with granular security policies and multi-agent reasoning capabilities.
The technical proposal is based on a microservices architecture that clearly separates orchestration and diagnosis responsibilities. The orchestrator, built with Antigravity SDK, acts as the entry point and applies a least-privilege security policy: by default it denies any action, and only allows delegating tasks to a specialized SRE sub-agent. This sub-agent, implemented with ADK v2, uses a two-node graph: the first node analyzes recent application traces, filters those with errors or high latencies, and isolates the conflicting trace ID; the second node correlates the spans and logs associated with that trace, executes metric queries, and generates a bottleneck analysis by calculating exclusive times. The result is a precise diagnosis that identifies not just the slow service but the specific span where time is lost, whether a database, an HTTP call, or a connection timeout.
To allow the agent to operate safely in real environments, the deployment on Google Cloud Run assigns independent service accounts with strictly read-only permissions for the diagnostic agent and write-only for the target application. This separation prevents the agent from mutating the production system while investigating. Additionally, the inventory agent provides a topological map of the project—services, dependencies, databases—allowing the search to be narrowed and avoiding unnecessary API calls. The entire flow communicates via the Agent-to-Agent (A2A) protocol and results are streamed as Server-Sent Events, enabling the engineer to follow the investigation in real time without waiting for a spinner.
From a business perspective, this type of solution fits perfectly with the needs of companies seeking custom applications for their operations. At Q2BSTUDIO we develop personalized software that integrates AI agents, process automation, and cloud platforms. An autonomous SRE agent drastically reduces mean time to resolution (MTTR) and frees on-call engineers from repetitive tasks, allowing them to focus on architectural improvements. Moreover, the underlying cloud infrastructure—whether AWS, Azure, or GCP—benefits from this kind of automation because the agent can scale horizontally without manual intervention.
Security is another fundamental pillar. The use of deny-by-default policies and permission segregation prevents a misconfigured agent from causing damage. Therefore, we recommend combining this approach with cybersecurity services that periodically audit agent accesses and rules. Likewise, the ability to generate automated post-mortem reports—with timeline, root cause analysis, and prevention plan—facilitates continuous improvement and accountability.
In terms of data analytics, SRE agents can integrate with Business Intelligence tools such as Power BI to visualize incident trends, response times, and recurring bottlenecks. This allows operations and business teams to make informed decisions about infrastructure investments or architectural changes. Artificial intelligence not only accelerates diagnosis but also uncovers hidden patterns that would go unnoticed by a human.
To test this architecture locally, a simulated mode exists that does not require cloud credentials. Simply clone the repository, install dependencies, and run the simulation script to see how the agent analyzes a database incident, identifies the bottleneck (for example, a connection timeout consuming 99% of the time), and generates a complete post-mortem report in Markdown. That report can then be exported with a single click to include in team documentation.
The system's extensibility is one of its greatest advantages. Being based on ADK and the A2A protocol, new sub-agents can be added—for instance, one that reviews security logs or queries database performance metrics—without modifying the main orchestrator. This makes it a future-proof platform where AI agents become the first line of response to any anomaly.
At Q2BSTUDIO we advocate the combination of cloud AWS/Azure, BI/Power BI, and AI agents as levers for digital transformation. An autonomous SRE agent is not just a technical tool but a strategic investment that reduces operational costs, improves service availability, and increases operations team satisfaction. If you are considering implementing a similar solution, our team can help you design an agent tailored to your infrastructure, with the security policies and diagnosis flows that best fit your business.
