GreenOps

Inspiration Cloud waste isn’t just a cost problem—it’s a carbon problem . While working with GCP environments, we discovered that teams often over-provision resources "just to be safe," leading to sky-high bills and unnecessary CO₂ emissions . Worse, optimizing manually is slow and error-prone. We built GreenOps to automate sustainability. Our vision: an AI team that continuously audits, forecasts, and optimizes cloud infrastructure What it does Imagine typing a question like: “How can I reduce cost and emissions in us-central1?” GreenOps activates. Here’s what happens: The GreenOps Agent , our commander-in-chief, kicks into action. It routes the query to a sequence of expert sub-agents —each trained for a precise role: scout, analyze, recommend, forecast, automate, summarize. Within moments, you get: Detailed infrastructure recommendations Forecasted resource + carbon usage Execution plan (if it’s safe) Weekly report in Google Docs Downloadable Presentation deck 🧠 How Each Agent Works 🔎 optimization_advisor_agent The strategist. Executes a three-agent relay: infra_scout_agent – Builds & runs BigQuery SQLs for region data workload_profiler_agent – Detects idle resources, forecasts emissions with Climatiq recommender_agent – Crafts a clean recommendation deck 📈 forecasting_tool_agent Predicts CPU, memory, and carbon emissions. Uses BigQuery ML models under the hood and returns formatted results. ⚖️ impact_calculator_agent "What if we moved from E2 to N2 in europe-west1?" This agent answers using cost and carbon deltas . 🛡️ safe_executor_agent Before executing changes, it forecasts risks . If safe, it: Stops instance Modifies type Restarts instance Autopilot meets green governance. 📊 summary_generator_agent Gathers regional data → Runs forecasts → Gets recommendations ➡ Generates beautiful Google Docs reports , including: Carbon trend charts CPU vs Carbon overlays Regional underutilization maps 🖼️ presentation_generator_agent Auto-summarizes reports and generates slide decks (with visuals!) using python-pptx , uploaded to Google Drive . 🧠 It’s your executive briefing... automated. Auto Generated Content Summary Report : Click Here Presentation Deck : Click Here (Microsoft Office Format, or ONLYOFFICE.app for MAC) How we built it We used the Google ADK to build modular, memory-aware agents that pass data between each other like a well-run team. ADK’s SequentialAgent and ToolAgent features allowed us to manage complex flows with simplicity. Technologies Used: Google ADK – Core multi-agent framework Vertex AI – Smart LLM-driven decision-making (Gemini) BigQuery – Data storage + forecasting ML models Climatiq – Emissions API Cloud Run – Serverless deployment of backend + agents Streamlit – Frontend interface Google Docs + Drive APIs – Auto-generated reports and slides Google Secret Manager – Secure API key storage Challenges we ran into True multi-agent logic : Designing agents that not only talk to each other but understand the sequence and context . Forecast before action : We wanted safety-first automation. That meant building forecast-aware logic for each recommended change. Dynamic document generation : Building reports and slides that don’t look like they came from a bot . Chaining LLM outputs : Ensuring agent-to-agent memory is passed correctly, especially in sequential chains with deeply nested outputs. Accomplishments that we're proud of Production-ready multi-agent orchestration (not just a demo) Cross-agent memory , forecasting, and safe execution logic Climate-conscious AI —bridging DevOps and sustainability Dynamic document + slide generation with AI Completely autonomous workflows , from question → insight → execution What we learned Good agents = good architecture : Clear responsibilities, tight loops, and minimal memory leaks. Forecasts are critical : We can’t trust AI to make infra changes without good predictive signals. Presentation generation with python-pptx is awesome but tricky : Especially when blending LLM text and chart data dynamically. ADK unlocks real-world agent coordination : It's more than a playground—it's a production-ready toolkit. What's next for GreenOps FinOps Agent : Detect budget anomalies and alert teams. Agent Self-Training : Use past optimization data to refine future decisions. 🧠 New agents : security_auditor_agent → for compliance and exposure risks cost_anomaly_detector_agent → alert on sudden usage spikes 🏆 Final Words GreenOps isn’t just smart—it’s strategic, sustainable, and scalable . With Google ADK as the brain, GCP as the spine, and AI as the soul, we’ve built something your cloud deserves . Let’s make DevOps greener, together. 🌍⚡