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ARCHITECTURE.md

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@@ -36,7 +36,9 @@ ChaosPilot is a full-stack AI platform for log analysis, incident detection, and
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- **Agent Manager:**
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- Receives user requests and delegates to specialized sub-agents.
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- **Agent Handoffs:**
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- Workflows are designed for agent handoff (e.g., detector → planner → fixer/notifier).
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- Workflows are designed for dynamic agent handoff (e.g., detector → planner → fixer/notifier). If a sub-agent determines another agent is better suited for the next step, it delegates the task.
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- **Sub-Agent LLM Access:**
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- Sub-agents can directly invoke Gemini (Google AI Platform) for LLM-powered analysis, classification, and planning, not just the main agent manager.
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- **Dynamic Toolsets:**
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- Each agent can invoke tools from the ADK toolbox, with toolsets defined per agent type.
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- **Frontend Visualization:**
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## 4. Google Cloud & AI Services (including Gemini)
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- **BigQuery:**
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- Stores and queries logs, incident data. Agents use BigQuery for analytics and context retrieval.
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- Stores and queries logs, incident data. Agents use the mcp-toolbox to connect to BigQuery, retrieve logs, and perform analytics and context retrieval.
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- **Cloud Logging:**
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- Ingests and manages raw logs. Scripts in `/scripts/` support log injection and management.
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- Ingests and manages raw logs. Logs are exported (sunk) from Cloud Logging to BigQuery for structured querying and analysis. Scripts in `/scripts/` support log injection, management, and sink setup.
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- **Gemini LLM (Google AI Platform):**
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- Backend calls Gemini for advanced log analysis, incident classification, and remediation planning.
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- Sub-agents and the main agent manager can each call Gemini for advanced log analysis, incident classification, and remediation planning.
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- All LLM calls are backend-only. There is currently no retrieval-augmented generation (RAG) pipeline, embedding generation, or vector similarity search implemented in the codebase. If RAG is implemented in the future, it will follow strict security and privacy guidelines.
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- **ADK Toolbox:**
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- All tools and toolsets are defined for use by agents, ensuring schema compliance and dynamic extensibility.
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- All tools and toolsets are defined for use by agents, ensuring schema compliance and dynamic extensibility. The mcp-toolbox provides the interface and schema for agents to interact with BigQuery and other data sources.
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3. Frontend sends authenticated request to FastAPI backend.
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4. Backend authenticates and invokes the main ADK agent.
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5. Agent manager delegates to the appropriate sub-agent.
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6. Sub-agent queries BigQuery, retrieves relevant logs, and may send those logs or summaries to the LLM (Gemini or Azure) for analysis.
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7. Agent manager may hand off to other agents as needed.
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6. Sub-agent queries BigQuery (via the mcp-toolbox), retrieves relevant logs, and may send those logs or summaries to Gemini for LLM-powered analysis.
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7. Agent handoff is dynamic: if a sub-agent determines another is better suited for the next step, it delegates the task.
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8. Backend streams response to frontend, which visualizes the multi-agent workflow.
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