Autonomous AI Ops Platform

OpsAI Splunk Dashboard
AI Analysis on splunk

Inspiration

At 3 AM, when a payment service crashes, engineers spend 2–4 hours manually searching Splunk logs to find what broke. We wanted to eliminate that entirely — not just speed it up, but make human intervention unnecessary.

What it does

Our platform sits on top of Splunk and autonomously investigates, diagnoses, and fixes incidents in under 30 seconds. Six specialized AI agents run in parallel:

Query Agent — fetches telemetry from Splunk
RCA Agent — identifies root cause using Groq AI
Risk Agent — scores severity from 0–100
Correlation Agent — maps blast radius across services
Timeline Agent — reconstructs when failure began
Remediation Agent — executes fix playbooks automatically

Engineers can also ask plain-English questions via a streaming AI copilot — no SPL knowledge needed.

Result: Mean Time To Resolve drops from 2–4 hours → 28 seconds (99.7% faster)

How we built it

We combined three technologies that had never been integrated together:

Splunk Enterprise for log ingestion and search
MCP (Model Context Protocol) to expose Splunk as AI-callable tools
Groq API (Llama 3.3 70B) for fast multi-agent reasoning

The agents run asynchronously in Python 3.12. AI-generated intelligence is written back into Splunk dashboards, creating a closed feedback loop. We also added Isolation Forest ML for anomaly detection and NetworkX for service causality graphs.

Challenges we ran into

Making 6 agents coordinate without conflicts or duplicate Splunk queries
Writing AI-generated findings back into Splunk in a structured, queryable format
Getting sub-10-second end-to-end response time with full reasoning depth
Building the MCP tool registry to work reliably with async agent calls

Accomplishments that we're proud of

First platform combining MCP + multi-agent AI directly on Splunk
Bidirectional Splunk integration — reads logs AND writes AI intelligence back
Full self-healing loop with zero human intervention required
Plain English copilot that makes Splunk accessible to non-engineers

What we learned

MCP is a game-changer for connecting AI agents to enterprise data platforms. Groq's speed (sub-second inference) is what makes real-time agentic ops actually practical. The hardest part wasn't the AI — it was orchestrating agents to not step on each other.