Agentic Navigator

The Multi-Agent Architecture for Knowledge Synthesis

Elevator Pitch

Agentic Navigator is an AI-powered multi-agent knowledge explorer that turns complex information—like research papers, documentation, or codebases—into clear, actionable insights. Built with Google's Agent Development Kit and deployed on Cloud Run, it uses cooperative agents powered by Gemini or Gemma to summarize, connect, and visualize knowledge in real time. Think of it as your intelligent research team in the cloud—analyzing, reasoning, and communicating together so you can focus on discovery, not data overload.
AI-Powered Multi-Agent System Google Cloud Run Real-Time Analysis
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Problem & Solution

Specialized Agent Collaboration

The Challenge: Unstructured Data & Cognitive Overload

Information Ingestion

Professionals overwhelmed by volume of unstructured, domain-specific data (technical docs, dense codebases, research papers).

Analysis Friction

Traditional methods struggle to extract cross-document relationships and synthesize coherent, human-actionable knowledge graphs.

The Black Box Problem

Existing AI solutions lack transparency in their reasoning process.

The Solution: Specialized Agent Collaboration

We simulate an expert human research team using a multi-agent system, where each agent is assigned a specialized, auditable role.

Agent Role Core Function
Orchestrator Manages the session, directs workflow, and handles user I/O.
Summarizer Generates concise, foundational text abstracts.
Linker Extracts key entities and verifiable relationships (core reasoning).
Visualizer Renders structured relationship data into an interactive graph format.
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Application Architecture

Serverless, Multi-Region Deployment on Google Cloud Run

Modern microservices architecture with clean separation of compute and state, managed end-to-end with Infrastructure as Code.

Component Technology Region Purpose
Frontend React/TypeScript, bun, Nginx us-central1 Low-latency dashboard for real-time visualization (FR#020).
Backend/Agents FastAPI, Python, ADK, A2A Protocol europe-west1 Orchestrates agents and manages the session lifecycle.
Gemma GPU Service FastAPI, PyTorch, Gemma 7B-IT europe-west1 Dedicated service for compute-intensive model inference.
State Layer Firestore NoSQL Global Persistent session memory, context synchronization, and knowledge caching (FR#029).
Deployment Terraform Cloud, GitHub Actions N/A Immutable Infrastructure as Code (IaC) and Workload Identity Federation (WIF).
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Core Agent & Model Architecture

The A2A Protocol & GPU-Accelerated Inference

Two-plane design to separate low-latency control from high-compute inference, secured by Workload Identity.

Control Plane (ADK/A2A)

  • Orchestrator initializes the workflow.
  • A2A Protocol sends SessionContext to the Summarizer.
  • Summarizer updates context via Firestore (FR#029).
  • A2A Protocol forwards context to the Linker Agent.
  • Linker updates context with entities/relationships.
  • A2A Protocol forwards context to the Visualizer.
  • Visualizer converts relationships to final Graph JSON.

Inference Plane (GPU Acceleration)

  • Linker Agent identifies the need for deep reasoning.
  • Linker Agent calls the Gemma GPU Service for:
    • Semantic Embeddings (input into relationship mapping).
    • Complex Reasoning (using the Gemma 7B-IT model).
  • Hardware: NVIDIA L4 GPU on Cloud Run (europe-west1).
  • High-Level Reasoning: Orchestrator/Agents use Gemini 1.5 Pro for abstract tasks.
  • Security: All service-to-service calls secured by Workload Identity (WI).
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Engineering Stack & Security

Toolchain and Best Practices for Production Readiness

Category Primary Tools Purpose & Benefit
IaC & CI/CD Terraform Cloud, GitHub Actions, Podman Immutable infrastructure, controlled by remote state. Podman ensures production/local container parity.
Identity & Auth Workload Identity Federation (WIF)
Workload Identity (WI)		 Secure, keyless deployment from GitHub Actions. Automatic authentication for Cloud Run services to access Firestore and Secret Manager.
Frontend/Backend TypeScript, React, bun, FastAPI, uv Maximized development velocity, type safety, and modern dependency management.
Model Serving PyTorch/CUDA, FastAPI Optimized model loading and inference logic for the dedicated GPU hardware (FR#028).
Persistence Firestore Fault-tolerant session memory, knowledge caching, and externalized prompt management (FR#003/FR#029).
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Conclusion & Path Forward

Delivering transparent, scalable, cloud-native AI reasoning

Key Accomplishments

✓ Validated Architecture

Successfully architected a multi-agent system fully supported by Google ADK and A2A Protocol.

✓ GPU Acceleration

Integrated NVIDIA L4 GPU into a serverless Cloud Run environment for compute-intensive AI tasks.

✓ Security Posture

Implemented WIF and WI for zero-trust security across the entire deployment lifecycle.

Next Steps & Future Enhancements

Phase Objective
Finalization Launch the Interactive Agent Collaboration Dashboard (FR#020).
Outreach Publish YouTube Walkthrough (FR#035): "How multi-agent systems can collaborate on Cloud Run."
Expansion Enable custom agent plug-ins and integrate multimodal inputs (e.g., Veo, Gemini Multimodal).

Agentic Navigator

Delivering complex, collaborative AI reasoning as a transparent, scalable, cloud-native service.

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