Sentinel — Autonomous System Health Guardian

Sentinel is a multi-agent infrastructure monitoring platform for a cloud-native environment. Beyond investigating anomalies in real time, it also acts as the platform's natural-language interface to operational telemetry, letting administrators query infrastructure health without writing SQL.

Four specialized agents — Monitor, Diagnostician, Remediation, and Data Intelligence — collaborate through a LangGraph state machine, coordinated entirely through a single standardized MCP gateway.

Architecture

The core workflow is a circular LangGraph state machine: Monitor detects anomalies, Diagnostician determines a likely root cause using a RAG knowledge base, Remediation attempts an appropriate fix (respecting safety guardrails), and Verify confirms whether the fix worked. If verification fails, the graph loops back to Diagnostician, up to a configurable retry limit.

All agents interact with the system exclusively through an MCP (Model Context Protocol) gateway — no agent talks to the database, the logs, or the knowledge base directly. This keeps every tool call auditable and swappable behind one interface.

Features

• Four CrewAI agents, each scoped to only the MCP tools its role requires
• RAG-backed diagnosis over a curated set of operational runbooks, using Sentence-Transformers embeddings and FAISS similarity search
• Natural-language-to-SQL interface over the infrastructure telemetry database, with graceful degradation for out-of-scope questions
• Safety guardrails that block high-risk remediation commands (e.g. service reboots) when the system is under critical load — verified to block correctly under real conditions, not just in theory
• Self-healing retry loop: failed verifications automatically route back to diagnosis rather than silently failing
• Full observability via LangSmith tracing on every NL2SQL call

Tech stack

Layer	Tools
Agent orchestration	CrewAI
Workflow / state machine	LangGraph
Tool gateway	FastMCP
LLM inference	Groq (openai/gpt-oss-120b)
Embeddings + retrieval	Sentence-Transformers (all-MiniLM-L6-v2), FAISS
Observability	LangSmith
Database	SQLite

Setup

python -m venv venv
venv\Scripts\activate          # Windows
pip install -r requirements.txt

Create a .env file with: GROQ_API_KEY=your_key LANGCHAIN_TRACING_V2=true LANGCHAIN_API_KEY=your_langsmith_key LANGCHAIN_PROJECT=sentinel-agent-system

Running a demo

python demo.py

This runs the full pipeline against a sample server, printing each agent's reasoning as it moves through Monitor → Diagnose → Remediate → Verify, including a live demonstration of the retry loop if verification fails on the first attempt.

Deliverables note

The Post-Mortem Report, LangSmith NL2SQL trace screenshots, and workflow diagrams were submitted separately, per assignment instructions.