Day 14 challenge

Goal: create data intelligence agents for graph and document databasesTheme: domain specialization week - data & analyticsTime investment: ~20 minutes
Welcome to Day 14! Today you’ll build data intelligence agents that work with modern databases—Neo4j for graph relationships and MongoDB for document structures. These agents understand complex data patterns, not just queries.

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What you’ll accomplish today

  • Build agents that understand graph relationships and document structures
  • Connect Neo4j for relationship analysis and pattern detection
  • Integrate MongoDB for flexible document querying
  • Create insights from complex, interconnected data
You’ll need access to Neo4j and/or MongoDB instances to complete today’s hands-on exercises. The concepts apply to any modern database.

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Step 1: Understanding modern data patterns

Data intelligence agents must grasp:

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Graph intelligence Neo4j

  • Relationship mapping: How entities connect and influence each other
  • Pattern detection: Finding hidden connections in networks
  • Path analysis: Shortest paths, influence chains, impact radius
  • Community detection: Clusters, groups, and anomalies

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Document intelligence MongoDB

  • Schema flexibility: Varying document structures and embedded data
  • Aggregation patterns: Complex queries across nested documents
  • Time-series analysis: Historical patterns and trends
  • X insights: Location-based patterns and relationships
Beyond SQL thinking modern data agents understand relationships and document structures, not just tables and rows.

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Step 2: Create specialized data agents

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Graph intelligence agent (Neo4j)

I want to create a customer intelligence agent using our Neo4j graph database.

The agent should:
- Analyze customer relationship networks and referral chains
- Identify influential customers based on connection patterns
- Find communities and clusters in our user base
- Detect unusual relationship patterns that might indicate fraud
- Recommend connections that could drive growth

Think like a network analyst who understands social dynamics and graph theory.

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Document analytics agent (MongoDB)

I want to create a product analytics agent using our MongoDB database.

The agent should:
- Analyze product usage patterns across different user segments
- Track feature adoption through nested event documents
- Identify user journeys from unstructured activity logs
- Find patterns in customer feedback and support tickets
- Generate insights from varying document schemas

Think like a data scientist working with semi-structured data.

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Step 3: Connect and explore your databases

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Neo4j connection capabilities

Your agent can:
  • Traverse relationships: Follow connections multiple degrees deep
  • Find patterns: Match complex relationship structures
  • Calculate centrality: Identify important nodes in networks
  • Detect communities: Find natural groupings and clusters

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MongoDB connection capabilities

Your agent can:
  • Query nested documents: Access deeply embedded data
  • Aggregate flexibly: Group, filter, and transform documents
  • Handle variety: Work with different document structures
  • Process time-series: Analyze temporal patterns

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Step 4: Implement data intelligence workflows

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Graph analysis example (Neo4j)

Who are our most influential customers based on referral networks?
How can we leverage these relationships for growth?
Your agent analyzes:
  • Referral chains and their success rates
  • Network effects from key customers
  • Community structures and growth patterns
  • Opportunities for connection strategies

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Document analysis example (MongoDB)

What user behavior patterns predict successful feature adoption?
Which segments are struggling with our new features?
Your agent examines:
  • Feature usage sequences in activity logs
  • Correlation between user properties and adoption
  • Time-to-value patterns across segments
  • Drop-off points in user journeys

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Step 5: Create advanced data workflows

Network health monitoring (Neo4j): 
Weekly, analyze our customer network for:
- Growing communities that might need dedicated support
- Isolated users who could benefit from connections
- Referral chains that have gone cold
- Emerging influencers based on connection growth

Provide actionable recommendations for community management.
Behavioral intelligence (MongoDB): 
Daily, process user activity documents to identify:
- Unusual usage patterns indicating potential issues
- Feature combinations that drive engagement
- User segments with declining activity
- Success patterns we should replicate

Focus on actionable insights, not just statistics.

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What you’ve accomplished

In 20 minutes, you’ve built sophisticated data intelligence agents: Graph understanding Agents that see relationships, not just data points Document flexibility working with varied, nested, and complex structures Pattern recognition finding insights in connected and unstructured data Actionable intelligence recommendations based on data patterns, not just queries

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The power of modern data agents

Data agents that understand modern databases can:
  • Reveal hidden connections in relationship networks
  • Find patterns in chaos of unstructured documents
  • Predict behaviors from historical patterns
  • Enable data democracy without query language expertise

Tomorrow - Day 15

Customer relationship management with Attio and Google Sheets. Build agents for sales intelligence and spreadsheet automation.

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Pro tip for today

Challenge your data agents: 
What non-obvious patterns in our data could give us a competitive advantage?
Show me connections or trends I wouldn't think to look for.
This pushes agents beyond basic queries to true intelligence.
Time to complete: ~20 minutes Skills learned graph database intelligence, document database analytics, pattern recognition, modern data workflows Next Day 15 - CRM and spreadsheet intelligence with Attio and Google Sheets
Remember the best data agents don’t just run queries—they understand the meaning behind the data and find patterns humans might miss.