"Smart-Grid Agent: Autonomous FinOps Orchestrator"

Smart-Grid Agent: Autonomous FinOps OrchestratorInspirationIn today's cloud-native landscape, the gap between observability (seeing a problem) and action (fixing it) is where efficiency dies. My inspiration came from the pain of manual infrastructure maintenance: I wanted to build an agent that doesn't just alert a human, but handles infrastructure optimization autonomously—like a "Self-Driving" system for server resources.How I Built ItThe Brain: I leveraged Google Cloud Agent Builder combined with Gemini 3. The agent acts as an autonomous engineer that reasons about system performance metrics.The Observability Layer: I used the Dynatrace MCP (Model Context Protocol) server. This allows the AI to query real-time infrastructure data (CPU, Memory, Disk I/O) as if it were reading its own sensory inputs.The Executor: Built in Python, the agent communicates with the underlying Linux kernel to identify non-critical, resource-heavy processes and safely terminates or throttles them to maintain optimal system health.Challenges I FacedThe biggest hurdle was the "Agentic Trust Barrier." Giving an AI system the power to terminate processes on a Linux machine requires a robust "Confirmation Loop." I solved this by implementing a structured decision-tree where the AI must output a Plan before the Execution is allowed to proceed.What I LearnedBuilding this taught me how to effectively map unstructured observability data (Dynatrace logs) into a structured format that a Large Language Model can use to make deterministic decisions.$$\text{Optimization Logic} \implies \text{Metric Query} \rightarrow \text{Reasoning} \rightarrow \text{Action}$$

Built With

• gemini-3
• google-vertex-ai-observability/monitoring:-dynatrace
• languages:-python-3.12-ai/ml:-google-cloud-agent-builder
• model-context-protocol-(mcp)-operating-system:-cachyos-(linux)-development-tools:-git
• vs