Use Cases and Personas for Agent Factory on Hybrid Manager#
Overview#
Agent Factory on Hybrid Manager enables diverse organizational use cases through sovereign AI infrastructure. Organizations leverage these capabilities to build intelligent applications while maintaining complete control over data and models within their Kubernetes environments.
Key Use Cases#
Internal Knowledge Management#
Organizations deploy AI assistants that provide employees with natural language access to internal documentation, policies, and institutional knowledge.
Implementation Pattern
Financial services firms index compliance documentation, regulatory filings, and internal procedures into knowledge bases. Employees query these systems using conversational interfaces, receiving accurate answers with source citations while maintaining data sovereignty.
Technical Components
Knowledge bases indexing SharePoint, Confluence, and database content
LLM endpoints for query understanding and response generation
Retrieval systems with role-based filtering for access control
See Getting started with Pipeline Designer for implementation guidance.
Customer Service Automation#
AI-powered customer support systems handle routine inquiries while escalating complex issues to human agents.
Implementation Pattern
E-commerce companies deploy assistants that access order systems, product catalogs, and return policies through secure APIs. These assistants resolve a significant portion of customer inquiries automatically while maintaining full audit trails for quality assurance.
Technical Components
REST API tools for order management system integration
Knowledge bases containing product documentation and FAQs
See Langflow quickstart for a working example.
Enterprise RAG Systems#
Retrieval-augmented generation applications ground LLM responses in organizational data, reducing hallucination while maintaining accuracy.
Implementation Pattern
Healthcare organizations implement RAG systems over clinical guidelines, research papers, and treatment protocols. Medical professionals receive evidence-based recommendations with citations to source materials, supporting clinical decision-making while ensuring traceability.
Technical Components
Vector embeddings stored in PostgreSQL with pgvector
Hybrid search combining semantic and keyword matching
Reranking models for result optimization
See Knowledge bases and retrieval for detailed architecture.
AI-Enabled APIs#
Organizations expose AI capabilities through secure APIs for integration with existing applications.
Implementation Pattern
Software companies provide intelligent features within their products by deploying model endpoints accessible through standard REST interfaces. These APIs handle tasks like document summarization, sentiment analysis, and content generation while maintaining predictable performance characteristics.
Technical Components
InferenceServices exposing OpenAI-compatible endpoints
API gateways with authentication and rate limiting
Load balancing across model replicas for high availability
See Model Serving Guide for deployment instructions.
User Personas#
Platform Engineers#
Platform engineers manage Agent Factory infrastructure, ensuring reliable model serving and optimal resource utilization.
Primary Responsibilities
Configure GPU nodes and resource allocation
Deploy and monitor InferenceServices
Manage model library and registry connections
Implement security policies and network configurations
Key Workflows
Provision GPU resources following GPU Setup Guide
Configure model registries per Model Library Documentation
Monitor inference performance using Observability (Agent Factory on HM)
Data Scientists#
Data scientists leverage Agent Factory to deploy models and build intelligent applications without managing infrastructure complexity.
Primary Responsibilities
Create and optimize knowledge bases
Configure retrieval strategies for RAG applications
Evaluate model performance and accuracy
Design assistant behaviors and tool integrations
Key Workflows
Build knowledge base pipelines using Getting started with Pipeline Designer
Configure retrieval in a Langflow flow: EDB Knowledge Base
Deploy flows as services: Flow deployment
Application Developers#
Developers integrate AI capabilities into business applications through APIs and SDKs.
Primary Responsibilities
Integrate model endpoints into applications
Build custom tooling for AI-powered features
Handle error conditions and fallback strategies
Key Workflows
Access model endpoints via KServe Integration
Call models from Python: Python Client Guide
Invoke deployed Langflow flows over HTTP or MCP: Accessing deployed flows
Business Analysts#
Business analysts use Langflow’s visual flow builder to create AI applications without coding expertise.
Primary Responsibilities
Define flow behaviors and knowledge domains
Configure data source connections
Test and validate flow responses
Monitor usage patterns and effectiveness
Key Workflows
Build flows using the Langflow visual editor: Langflow quickstart
Use pre-built templates as starting points: Langflow templates
Publish and deploy flows for others to use: Flow deployment
Industry-Specific Implementations#
Financial Services#
Use Case: Regulatory compliance assistant analyzing transaction patterns and generating required reports.
Components
Knowledge base of regulatory documentation
Tools accessing transaction databases
Audit logging for compliance tracking
Healthcare#
Use Case: Clinical decision support system providing evidence-based treatment recommendations.
Components
RAG over medical literature and guidelines
Integration with electronic health records
Strict access controls for patient data
Manufacturing#
Use Case: Maintenance assistant diagnosing equipment issues using sensor data and technical manuals.
Components
Knowledge base of equipment documentation
Real-time data integration from IoT sensors
Predictive maintenance model endpoints
Implementation Considerations#
Scalability Planning#
Different use cases require varying levels of scale:
High-volume customer service: Multiple model replicas with auto-scaling
Internal knowledge systems: Moderate scaling with caching optimization
Specialized assistants: Single instances with dedicated resources
Security Requirements#
Use cases determine security configurations:
Public-facing applications: External ingress with authentication
Internal systems: Cluster-local access with RBAC
Regulated industries: Enhanced audit logging and encryption
Performance Optimization#
Optimize based on use case characteristics:
Real-time applications: Low-latency model serving with edge caching
Batch processing: Higher throughput with larger batch sizes
Interactive assistants: Balanced latency and throughput
Getting Started by Use Case#
For Knowledge Management#
Create a knowledge base pipeline: Getting started with Pipeline Designer
Query the knowledge base from a flow: EDB Knowledge Base
Build a RAG flow end-to-end: Langflow quickstart
For Customer Service#
Build a conversational flow: Langflow quickstart
Try the Simple Migration Agent template as a reference for agentic flows: Langflow templates
Deploy the flow as a callable service: Flow deployment
For API Development#
Deploy models via Deploy Agent Factory with Hybrid Manager UI
Access endpoints: Python Client Guide
Monitor performance: Observability (Agent Factory on HM)
Additional Resources#
Related Documentation