personas.yaml

# Redpanda Cloud Documentation Personas
#
# These personas represent the target audience for Redpanda Cloud documentation.
# Use these when assigning :personas: attributes to documentation pages.
#
# This persona set covers two domains:
# 1. Streaming/Data Platform: Real-time data streaming, connectors, pipelines
# 2. Agentic Data Platform (ADP): AI agent development, governance, enterprise AI adoption

schema_version: "1.0"
repository: cloud-docs

personas:
  # ============================================================================
  # TIER 1: Executive & Governance
  # ============================================================================

  - id: executive
    name: Executive Stakeholder
    description: CIO/CAIO/Head of AI Strategy driving enterprise AI adoption and governance
    experience_level: executive
    goals:
      - Drive enterprise-wide AI adoption strategy
      - Ensure ROI on AI investments
      - Establish governance framework for agent deployments
      - Manage cost and resource allocation
      - Ensure compliance with organizational policies
    pain_points:
      - Lack of visibility into agent usage and costs
      - Difficulty enforcing governance at scale
      - Unclear ROI metrics for AI initiatives
      - Risk of shadow AI deployments
      - Integration with existing enterprise systems
    content_preferences:
      - High-level governance frameworks
      - ROI and cost analysis
      - Compliance and audit capabilities
      - Executive dashboards and reporting
      - Strategic planning guides
    typical_content_types:
      - overview
      - concepts
      - best-practices

  - id: security_leader
    name: Security & Risk Leader
    description: CISO/Compliance Officer protecting systems and enforcing data protection policies
    experience_level: advanced
    goals:
      - Enforce agent policy and access controls
      - Maintain audit trails for compliance
      - Protect sensitive data and credentials
      - Manage risk across agent deployments
      - Ensure regulatory compliance
    pain_points:
      - Agent access to sensitive systems
      - Lack of visibility into agent actions
      - Difficult to audit agent behavior
      - Credential management and rotation
      - Compliance with data protection regulations
    content_preferences:
      - Security architecture patterns
      - Policy enforcement mechanisms
      - Audit trail documentation
      - Compliance certification guides
      - Incident response procedures
    typical_content_types:
      - concepts
      - reference
      - best-practices
      - troubleshooting

  # ============================================================================
  # TIER 2: Platform Operations
  # ============================================================================

  - id: platform_admin
    name: Platform Administrator
    description: Manages Redpanda Cloud clusters, users, security, and billing
    experience_level: intermediate
    goals:
      - Provision and configure clusters
      - Manage user access and security
      - Monitor cluster health and usage
      - Control costs and optimize resources
    pain_points:
      - Complex security configuration
      - Understanding billing and usage metrics
      - Managing access across teams
      - Capacity planning
    content_preferences:
      - Step-by-step administration guides
      - Security best practices
      - Billing and cost optimization tips
      - Monitoring and alerting setup
    typical_content_types:
      - how-to
      - reference
      - best-practices

  - id: ai_platform_engineer
    name: AI/ML Platform Engineer
    description: Operates agent infrastructure, runtimes, and connectivity with governance controls
    experience_level: advanced
    goals:
      - Deploy and operate agent runtime infrastructure
      - Configure governance controls and policies
      - Monitor agent performance and resource usage
      - Onboard and manage MCP servers
      - Ensure agent observability and debugging
    pain_points:
      - Complex agent runtime configuration
      - Difficult to troubleshoot agent failures
      - Managing agent resource allocation
      - Integrating governance with existing tools
      - Scaling agent infrastructure
    content_preferences:
      - Infrastructure setup guides
      - Governance configuration patterns
      - Observability and monitoring setup
      - Performance tuning documentation
      - Troubleshooting workflows
    typical_content_types:
      - how-to
      - reference
      - troubleshooting
      - best-practices

  # ============================================================================
  # TIER 3: Builders & Developers
  # ============================================================================

  - id: app_developer
    name: Application Developer
    description: Builds applications that produce and consume data from Redpanda Cloud
    experience_level: intermediate
    goals:
      - Connect applications to Redpanda Cloud clusters
      - Produce and consume messages reliably
      - Implement proper error handling and retries
      - Optimize client performance
    pain_points:
      - Authentication and connection configuration
      - Understanding Kafka client options
      - Debugging connectivity issues
      - Choosing the right client library
    content_preferences:
      - Working code examples in multiple languages
      - Connection configuration templates
      - Client library comparisons
      - Performance tuning guides
    typical_content_types:
      - how-to
      - tutorial
      - reference

  - id: agent_developer
    name: Agent Developer
    description: Builds AI agents, agentic workflows, and MCP tools that integrate with Redpanda Cloud and ADP
    experience_level: intermediate
    goals:
      # MCP and streaming integration
      - Create MCP tools that AI assistants can discover and use
      - Deploy MCP servers to Redpanda Cloud
      - Integrate with AI/LLM applications
      - Debug agent-tool interactions
      # Agentic workflows and governed deployment
      - Build agents and workflows that solve business problems
      - Use ADP catalog, templates, and curated datasets
      - Design reasoning patterns and tool interactions
      - Deploy agents into governed runtime
    pain_points:
      # MCP and integration challenges
      - MCP configuration syntax
      - Testing tools before deployment
      - Limited AI-specific examples
      # ADP and governance challenges
      - Hard to discover existing templates, MCP servers, datasets
      - Unclear access policies
      - Brittle multi-step integrations
      - Inconsistent testing/debugging environments
    content_preferences:
      # Code examples and patterns
      - Working code examples with AI context
      - Testing and debugging workflows
      - Integration patterns
      # Catalog and governance
      - Rich catalog of agent templates and tools
      - Governance introspection (what agent can/can't do)
      - Replay-based debugging
      - Streamlined deployment workflows
    typical_content_types:
      - tutorial
      - how-to
      - cookbook
      - best-practices

  - id: streaming_developer
    name: Streaming Platform Developer
    description: Works with Kafka/Redpanda streaming systems at scale
    experience_level: advanced
    goals:
      - Build event-driven architectures on Redpanda Cloud
      - Optimize for high throughput and low latency
      - Implement exactly-once semantics
      - Migrate from self-hosted Kafka
    pain_points:
      - Consumer group behavior differences
      - Schema registry configuration
      - Performance at scale
      - Migration complexity
    content_preferences:
      - Advanced configuration options
      - Performance tuning guides
      - Migration documentation
      - Architecture patterns
    typical_content_types:
      - concepts
      - how-to
      - reference
      - best-practices

  # ============================================================================
  # TIER 4: Data & Knowledge Management
  # ============================================================================

  - id: data_engineer
    name: Data Engineer
    description: Builds data pipelines with managed connectors AND creates curated datasets for agent consumption
    experience_level: intermediate
    goals:
      # Data movement and pipelines
      - Set up managed connectors to move data between systems
      - Transform and route data reliably
      - Monitor connector and pipeline health
      - Handle errors and retries
      # Agent-ready datasets and RAG
      - Create agent-ready datasets with federated SQL
      - Ensure data quality and freshness for agents
      - Expose data safely through governed views
      - Provide clean RAG context via MCP servers
    pain_points:
      # Connector and pipeline challenges
      - Connector configuration complexity
      - Debugging failed connectors
      - Schema management and evolution
      - Performance tuning
      # Data curation for agents
      - Siloed data across sources
      - Fragile RAG sources
      - Schema drift
      - Difficulty providing agent-ready datasets quickly
    content_preferences:
      # Connector and transformation
      - Connector setup guides
      - Transformation examples
      - Error handling patterns
      - Monitoring and troubleshooting
      # Federated data and RAG
      - Federated SQL query examples
      - Governed view patterns
      - RAG context design
      - Data lineage visualization
    typical_content_types:
      - how-to
      - cookbook
      - troubleshooting
      - reference

  - id: knowledge_manager
    name: Knowledge & Operations Manager
    description: Maintains organizational documentation and knowledge bases for agent consumption
    experience_level: intermediate
    goals:
      - Ingest and maintain organizational knowledge bases
      - Ensure content freshness and accuracy
      - Optimize vector search for agent queries
      - Manage knowledge base access and permissions
    pain_points:
      - Stale or outdated documentation
      - Difficult to index and search content
      - Managing content from multiple sources
      - Ensuring agent retrieval accuracy
    content_preferences:
      - KB ingestion workflows
      - Vector search optimization guides
      - Content freshness strategies
      - Access control patterns
    typical_content_types:
      - how-to
      - best-practices
      - troubleshooting

  # ============================================================================
  # TIER 5: Evaluation & End Users
  # ============================================================================

  - id: evaluator
    name: Technical Evaluator
    description: Assessing Redpanda Cloud for their organization
    experience_level: beginner
    goals:
      - Understand Redpanda Cloud capabilities quickly
      - Evaluate fit for specific use cases
      - Understand pricing and billing
      - Compare with alternatives
    pain_points:
      - Hard to find high-level overview
      - Too much detail too soon
      - Unclear pricing structure
      - Missing comparison information
    content_preferences:
      - High-level overviews
      - Use case examples
      - Quick start guides
      - Pricing documentation
    typical_content_types:
      - overview
      - concepts
      - tutorial

  - id: business_user
    name: Business End User
    description: Uses agent-powered automations to complete business tasks
    experience_level: beginner
    goals:
      - Complete tasks efficiently using agents
      - Understand what agents can and cannot do
      - Trust agent recommendations and actions
      - Report issues when agents fail
    pain_points:
      - Unclear agent capabilities
      - Unexpected agent behavior
      - Lack of transparency in agent actions
      - Difficulty getting help when agents fail
    content_preferences:
      - Simple, task-oriented guides
      - Agent capability overviews
      - Troubleshooting for common issues
      - Trust and transparency documentation
    typical_content_types:
      - overview
      - how-to
      - troubleshooting