Splunk Agentic Ops · agentic AIOps

TitanHelix

An autonomous reasoning mesh that sits on your Splunk data and explains failures while they unfold.

A feature-store database drifts to 94% CPU. It's slow, not broken — every query still returns 200 OK, so error-rate alerting stays quiet. Ninety seconds later checkout throws 503s and a region goes dark. TITAN HELIX already knew the database was the cause.

29 services6 reasoning agents 7 Splunk indexesMCP-native
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// what it actually does

Not another dashboard

Finds the cause, not the symptom

The root cause is CPU-bound but 200 OK — invisible to error-rate alerts. The mesh catches the silent climb before the first 503 ever fires.

Reasons in the open

Six specialist agents — observer, memory, correlation, prediction, remediation, executive — converge on a verdict, each citing the exact SPL that backs it.

Splunk-native, MCP-standard

Agents reach Splunk through the Model Context Protocol — typed tool calls, not glue code. Point it at a real deployment and the reasoning layer doesn't change a line.

// how it's wired

Architecture

TITAN HELIX architecture diagram

Telemetry enters Splunk over HEC across seven indexes and is queried back over SPL — the graph and every drill-down are SPL results, not a side database. The AI mesh reasons over those results and reaches Splunk through MCP, so the model calls typed tools instead of hard-coded queries. Open the full animated diagram →

See it diagnose a live failure

Forty seconds, no install — it plays the whole cascade in your browser.

▶ Launch the demo
01Live dependency graph