As a developer working with distributed microservices, I've always faced the challenge of getting a unified view across business metrics and infrastructure health. When Google announced the GKE Turns 10 Hackathon focused on AI agents, I saw the perfect opportunity to solve this problem with Agentic Fusion - a business intelligence layer that brings AI-powered insights to Kubernetes environments.
The Problem with Microservices Visibility
Modern applications are built using microservices architecture, which brings great scalability but creates data silos. Business teams struggle to get insights from fragmented databases, while DevOps teams monitor infrastructure metrics in isolation. What we needed was a single source of truth that could bridge both domains and let AI assist in decision-making.
What Agentic Fusion Does
Agentic Fusion is an open-source application that deploys alongside your existing Kubernetes infrastructure to provide:
Unified Data Collection: The system automatically scans your Kubernetes cluster to collect infrastructure logs, server configurations, database schemas, and business metadata from your microservices.
AI-Powered Analytics: Using Google's Gemini AI models, it can answer complex queries like "What are the sales patterns in the last quarter?" or "How many K8s pods are running for payment services?" The responses include intuitive visualizations and charts generated on-the-fly.
Micro-Agent Architecture: Built using Google's Agent Development Kit (ADK), the system creates specialized AI agents for specific tasks - from analyzing checkout abandonment rates to monitoring CPU usage trends.
Automated Workflows: Chain multiple agents together to create automated workflows. For example, a stock fulfillment workflow that analyzes weekly sales, checks inventory, generates forecasts, and emails suppliers every Monday.
Technical Implementation
The architecture leverages several key technologies:
- Backend: Python with Flask for the web interface and FastAPI for agent orchestration
- Data Layer: PostgreSQL for persistent business data and SQLite for local caching
- AI Integration: Google's Agent Development Kit and Gemini models for natural language processing
- Infrastructure: Deployed on Google Kubernetes Engine with kubectl-ai for intelligent cluster management
The application is containerized and designed to be plug-and-play with existing GKE deployments. Once deployed, it automatically discovers microservices through Kubernetes APIs and starts building the unified data model.
Key Challenges and Solutions
Data Scanning at Scale: Efficiently scanning multiple microservices for business metadata required building a smart discovery system that could identify database connections, API endpoints, and configuration files without disrupting running services.
Context Management: Managing large volumes of logs and providing relevant context to Gemini without hitting token limits required implementing intelligent indexing and summarization techniques.
Agent Orchestration: Learning Google's ADK while building the micro-agent system was challenging but rewarding - the toolkit provides powerful abstractions for building conversational AI agents.
Real-World Impact
During testing with the eCommerce demo application, Agentic Fusion successfully identified user drop-off patterns in the transaction flow and automatically generated recommendations for improving the checkout experience. The infrastructure monitoring capabilities helped identify resource bottlenecks before they impacted performance.
What's Next
The hackathon was just the beginning. I'm working on improving the data management pipeline, integrating kubectl-ai more deeply for infrastructure automation, and preparing the official open-source release.
Agentic Fusion demonstrates how AI agents can transform both business intelligence and infrastructure management in Kubernetes environments. By creating that crucial bridge between business and technical domains, we're enabling smarter, data-driven decisions at every level.
The future of microservices isn't just about scaling applications - it's about scaling intelligence.
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