Incident Investigation Agent
Incident Health Dashboard
Ask Operational Agent
One-Click Outage Generation for demo
RCA and postmortem report generated by Agent

Incident Memory AI

Inspiration

Every engineering team has experienced the same frustrating scenario:

A production outage occurs.

Alerts start firing.

Engineers jump between dashboards, logs, tickets, Slack threads, and monitoring tools trying to answer a simple question:

"Have we seen this before?"

Ironically, the organization may have already solved the exact same problem weeks or months earlier, but that knowledge is buried inside logs, tickets, postmortems, and tribal memory.

We wanted to build a system that transforms incidents into organizational knowledge.

Instead of treating outages as isolated events, we asked:

What if every incident became searchable operational memory that could help solve future incidents automatically?

That idea became Incident Memory AI.

What It Does

Incident Memory AI is an AI-powered operational intelligence platform that helps engineering teams investigate production incidents using:

MongoDB Atlas as an Operational Memory Platform
MongoDB Atlas Vector Search
MongoDB MCP (Model Context Protocol)
Google Gemini
Agentic Investigation Workflows

The platform automatically:

Generates realistic outage scenarios
Ingests and enriches incident logs
Correlates related failures into incident clusters
Retrieves similar historical incidents using semantic search
Performs AI-powered Root Cause Analysis (RCA)
Builds incident timelines
Generates postmortems
Answers operational questions through a MongoDB-powered agent

How We Built It

Synthetic Traffic Service

We created a FastAPI-based synthetic traffic generator capable of producing realistic outage scenarios including:

Payment Database Failures
Authentication Failures
Latency Spikes
Cascading Service Failures

This allows us to simulate production incidents and create realistic operational datasets.

Ingestion & Enrichment Pipeline

Generated logs are ingested into MongoDB Atlas and enriched with:

Error types
Root causes
Resolution actions
Environment metadata
Service ownership

The platform converts raw telemetry into structured operational knowledge.

Operational Memory Layer

MongoDB Atlas serves as the central operational memory platform.

Collections include:

raw_logs
parsed_logs
synthetic_incidents
incident_clusters
postmortems

Rather than storing incidents as isolated records, we build a continuously growing knowledge base of operational history.

Semantic Incident Retrieval

Using sentence embeddings and MongoDB Atlas Vector Search, the platform can retrieve similar historical incidents from tens of thousands of incident records.

Engineers can search using natural language queries such as:

"payment database failure"
"checkout timeout"
"authentication token issue"

and immediately retrieve relevant historical incidents.

Incident Correlation Engine

Individual failures rarely occur in isolation.

We built an incident correlation engine that groups related failures based on:

Time windows
Root causes
Services
Environments

This produces outage-level incident clusters that provide a much more realistic view of system failures.

MongoDB MCP Integration

One of the most exciting parts of the project is our MongoDB MCP integration.

We built an Operational Agent that allows Gemini to reason over operational memory stored in MongoDB Atlas.

The agent can answer questions such as:

Which services experience the most HIGH severity incidents?
What root cause appears most frequently?
Which environments are most unstable?
Show the latest incident clusters.

Instead of manually writing queries, engineers can interact with operational data using natural language.

AI-Powered Investigation Agent

We also built an Investigation Agent that performs multi-step incident analysis.

The workflow combines:

Vector Search
Operational Memory Retrieval
Incident Cluster Analysis
Timeline Reconstruction
MongoDB Evidence Extraction
Gemini Reasoning

The result is a grounded investigation report that includes:

Root Cause
Confidence Score
Evidence
Recommended Actions

AI RCA & Postmortems

Incident Memory AI automatically generates:

Root Cause Analysis

Including:

Root cause identification
Confidence scoring
Supporting evidence
Remediation recommendations

Postmortems

Including:

Executive Summary
Timeline
Root Cause
Impact Assessment
Resolution Details
Action Items

Challenges We Faced

One of the biggest challenges was moving beyond simple semantic search.

We wanted the platform to reason over incidents, not just retrieve them.

This required:

Building operational memory structures
Correlating failures into clusters
Grounding AI responses using MongoDB evidence
Designing investigation workflows that combine retrieval, aggregation, and reasoning

Another challenge was creating realistic incident scenarios and ensuring that generated incidents behaved like real production outages.

What We Learned

During development we learned:

How to use MongoDB Atlas as an operational memory platform
How Vector Search can dramatically improve incident retrieval
How MCP enables natural-language interaction with operational data
How agentic workflows can improve incident investigation
How grounding AI systems with operational evidence increases trust and accuracy

Impact

Incident Memory AI transforms incident response from:

Searching Logs → Searching Knowledge

By combining MongoDB Atlas, Vector Search, MCP, and Gemini, we created a platform where every incident makes the system smarter.

The result is faster investigations, lower Mean Time To Resolution (MTTR), and a continuously growing operational memory that helps engineering teams solve incidents more efficiently.