🚀 Introducing Generation 3 AI

AI That Thinks
Like Your Best DevOps Engineer

Combining Agentic RAG with Graph RAG for intelligent, context-aware infrastructure diagnosis that actually understands your systems.

+25%
Diagnosis Accuracy
+40%
Context Relevance
5-Step
Agent Reasoning
4 Tools
Dynamic Selection
The Evolution

From Simple RAG to Intelligent Agents

Traditional RAG is like a library search. Our Agentic RAG + Graph RAG is like having a senior engineer who knows your entire infrastructure.

Generation 1

Simple RAG

Vector search for similar documents. Fast but naive.

Fast (1s)
No context awareness
No relationships
Static queries
Accuracy: ~60%
Generation 2

Graph RAG

Understands relationships but lacks dynamic reasoning.

Context-aware
Relationship understanding
Static query paths
No self-correction
Accuracy: ~75%
Generation 3

Agentic + Graph RAG

Intelligent agent with dynamic tool selection and multi-step reasoning.

Dynamic tool selection
Multi-step reasoning
Self-correcting
Context + relationships
Accuracy: ~85% 🚀

Traditional Approach

Vector search only
Find similar documents, no context
Static pipeline
Always Step 1→2→3, no flexibility
No relationships
Doesn't know what connects to what

Agentic RAG + Graph RAG

4 tools: Graph, Time-series, Vector, APIs
Agent chooses what to use
Dynamic reasoning
Adapts strategy based on results
Full infrastructure graph
Neo4j topology + impact analysis
Agent Architecture

How the Intelligent Agent Works

The agent uses a ReAct (Reasoning + Acting) pattern to dynamically solve problems

System Architecture

Webhook → Agent → Tools → Diagnosis

Webhook
Alert arrives
Agent
Decides strategy
4 Tools
Execute queries
Neo4j
Graph topology
TimescaleDB
Time-series trends
Vector DB
Similar cases
External APIs
Additional context

Agent Reasoning Process (ReAct Pattern)

1
Thought: "I need to understand what runs on server-01"
Agent analyzes the webhook and decides first action
Action: neo4j_graph_query
Result: server-01 runs api-gateway, worker-service
2
Thought: "Has disk usage been growing?"
Agent checks for trends to understand severity
Action: timescale_timeseries_query
Result: Disk: 80% → 98% in 24h, will fill in 1.8h
3
Thought: "Was there a similar issue before?"
Agent learns from past incidents
Action: vector_semantic_search
Result: Similar on server-03, fixed by clearing logs
Final Answer: Comprehensive Diagnosis
Agent synthesizes all information
Severity: CRITICAL
Root Cause: Log accumulation in /var/log
Impact: api-gateway & worker-service will fail in 1.8h
Recommendations: 3 immediate + 2 long-term actions
Performance

The Numbers Don't Lie

Side-by-side comparison of three generations of RAG technology

Metric
Generation 1
Simple RAG
Generation 2
Graph RAG
Generation 3
Agentic + Graph
Diagnosis Accuracy 60% 75% 85% 🚀
Context Awareness
Multi-hop Reasoning Limited
Self-Correction
Avg Latency 1s 1.5s 4.5s
Tool Orchestration 1 tool 4 tools
User Satisfaction Baseline +15% +30%
+25%
Better Accuracy
vs Simple RAG
-70%
Reduced MTTR
Mean Time To Resolution
5x
More Context
Rich infrastructure data
Live Example

See It In Action

Watch how the agent diagnoses a complex database issue with 5 reasoning steps

Input: Webhook Alert 

POST /hook/db-diagnostics

// Webhook payload
{
  "database": "postgres-primary",
  "metric": "slow_queries",
  "avg_query_time_ms": 5000,
  "connections": 95,
  "max_connections": 100,
  "timestamp": "2025-11-14T14:00:00Z"
}

Agent Reasoning Trace

Iteration 1: neo4j_graph_query
→ Found: api-gateway, worker, analytics-job
Iteration 2: timescale (latency)
→ Query time: 100ms → 5000ms in 2h
Iteration 3: timescale (connections)
→ Connections spiked at 12:00 PM
Iteration 4: neo4j (job details)
→ analytics-job runs daily at 12:00 PM
Iteration 5: vector_search
→ Similar: connection leak, fix timeout

Output: AI Diagnosis

CRITICAL Diagnosed in 4.2s (5 iterations)

PostgreSQL overloaded by analytics-job connection leak

Root Cause

analytics-job started at 12:00 PM with aggressive queries. Job times out after 2 hours but doesn't close connections. 95 out of 100 connections are held by zombie processes.

Impact Analysis

api-gateway and worker-service are unable to get connections. New queries are queuing, causing 5s latency (normal: 100ms).

Recommendations
  1. Immediate: Kill zombie connections
  2. Short-term: Increase timeout 2h → 4h
  3. Medium-term: Implement PgBouncer
  4. Long-term: Reschedule to 2:00 AM
Confidence: 92% Based on: 3 graph queries + 2 time-series + 1 vector search

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