Use Cases for Agentic Systems
Use Cases for Agentic Systems
A structured map of the agentic AI landscape based on real-world implementations and production systems
Overview
This document captures the essential use cases, capabilities, and architectural patterns for agentic systems, derived from analysis of LangGraph implementations, production deployments, and community projects.
Overall Structure: 20 entities connected by 24 relationships across 4 major categories.
🎯 Use Case Categories
Core Application Domains
1. Conversational Agents
- Purpose: Human-like interaction and assistance
- Examples: ChatLangChain, customer service bots, personal assistants
- Key Requirements: Memory, context awareness, natural dialogue
- Production Users: Klarna (customer service), general chatbot platforms
2. Tool-Using Agents
- Purpose: External system interaction and automation
- Examples: ReAct agents, API orchestration, computer use automation
- Key Requirements: Function calling, reasoning about tool use
- Production Users: GitHub Copilot-style systems, automation platforms
3. Research & Analysis
- Purpose: Information gathering and synthesis
- Examples: Open Deep Research, academic research automation
- Key Requirements: Web search, data processing, multi-source analysis
- Production Users: Research institutions, competitive intelligence
4. Knowledge Management
- Purpose: Information storage, retrieval, and Q&A
- Examples: RAG systems, document assistants, knowledge bases
- Key Requirements: Vector search, semantic matching, context grounding
- Production Users: Enterprise documentation systems
5. Multi-Agent Coordination
- Purpose: Complex task orchestration across multiple agents
- Examples: Supervisor systems, swarm collaboration
- Key Requirements: Agent communication, task delegation, coordination
- Production Users: LinkedIn (workflow automation), Uber (distributed systems)
6. Development & Code
- Purpose: Programming assistance and automation
- Examples: SQL agents, code generation, automated testing
- Key Requirements: Code understanding, API integration, testing frameworks
- Production Users: Replit (coding assistance), development tools
7. Business Process Automation
- Purpose: Workflow automation and business logic
- Examples: Email management, approval workflows, scheduling
- Key Requirements: Process orchestration, human oversight, integration
- Production Users: Enterprise workflow systems
8. Security & Monitoring
- Purpose: Threat detection and incident response
- Examples: Automated security operations, log analysis
- Key Requirements: Real-time processing, pattern recognition, alerting
- Production Users: Elastic (threat detection), security operations centers
9. Creative & Content
- Purpose: Content generation and creative assistance
- Examples: Writing assistance, media production, design tools
- Key Requirements: Creativity evaluation, human collaboration
- Production Users: Content marketing platforms, creative tools
10. E-commerce & Finance
- Purpose: Customer service and transaction processing
- Examples: Financial advisory, fraud detection, personalized shopping
- Key Requirements: Regulatory compliance, security, scalability
- Production Users: Klarna (financial services), e-commerce platforms
⚙️ Core Capabilities
Essential Technical Components
| Capability | Purpose | Required By | Key Features |
|---|---|---|---|
| Memory & Context | Multi-turn conversations, personalization | Conversational Agents, Creative Content | Short-term, long-term, cross-session persistence |
| Human-in-the-Loop | Quality control, approval workflows | Business Process, Creative Content | Interrupts, approval gates, interactive guidance |
| Tool Integration | External system interaction | Tool-Using Agents, Research, Development | API calling, database queries, file operations |
| Planning & Reasoning | Complex task decomposition | Research, Development, Multi-Agent | Goal setting, strategy formulation, adaptive planning |
| Parallel Processing | Concurrent task execution | Multi-Agent, Security, Large-scale systems | Distributed coordination, load balancing |
Capability Dependencies
🏗️ Agent Architectures
Fundamental Design Patterns
1. ReAct Pattern
- Approach: Reasoning and Acting in cycles
- Structure: Thought → Action → Observation → Repeat
- Best For: Tool-using agents, single-agent systems
- Examples: Most LangGraph templates, SQL agents
2. Supervisor Architecture
- Approach: Central coordinator with specialized workers
- Structure: Supervisor → Route → Worker → Report → Supervisor
- Best For: Complex task distribution, quality oversight
- Examples: LinkedIn's production systems, Elastic's security orchestration
3. Swarm Architecture
- Approach: Peer-to-peer agent collaboration
- Structure: Agent ↔ Agent ↔ Agent (dynamic handoffs)
- Best For: Emergent behavior, flexible task distribution
- Examples: Research systems, distributed problem solving
4. Workflow Orchestration
- Approach: Explicit state machines with defined transitions
- Structure: Plan → Execute → Review → Approve → Complete
- Best For: Business processes, auditable workflows
- Examples: Open Deep Research (graph.py), approval systems
🔗 Integration Patterns
RAG Integration
- Purpose: Combine agents with knowledge retrieval
- Pattern: Query → Retrieve → Generate → Respond
- Applications: Knowledge management, document Q&A
- Examples: Retrieval agents, ChatLangChain
🚀 Deployment Models
Infrastructure Approaches
| Model | Characteristics | Best For | Examples |
|---|---|---|---|
| Local Deployment | Privacy, control, cost-effective | Personal use, sensitive data | Ollama Deep Research |
| Cloud Platform | Managed, scalable, enterprise features | Production systems | LangGraph Platform |
| Hybrid Architecture | Combined local + cloud | Custom requirements | Enterprise implementations |
📊 Operational Requirements
Production Considerations
Quality Control
- Components: Testing, validation, human review, monitoring
- Critical For: Finance, security, business processes
- Implementation: Automated testing, approval workflows, performance metrics
Scalability
- Components: Horizontal scaling, load balancing, fault tolerance
- Required By: E-commerce, finance, multi-agent systems
- Implementation: Cloud platforms, distributed architectures
🔄 Key Relationship Patterns
Dependency Chains
- Simple Agents: Tool Integration → ReAct Pattern → Specific Use Case
- Complex Systems: Planning → Multi-Agent → Parallel Processing → Scalability
- Enterprise Systems: Human-in-the-Loop → Quality Control → Production Deployment
Enhancement Relationships
- Memory & Context enhances Conversational Agents
- RAG Integration enhances Knowledge Management
- Planning & Reasoning enables Multi-Agent Coordination
Architecture Flows
🎯 Strategic Insights
Hub Nodes (Most Connected)
- Tool Integration: Central to most use cases
- Quality Control: Critical for production systems
- Multi-Agent Coordination: Enables complex orchestration
Domain Specialization Patterns
- High-Stakes Domains (Finance, Security): Require robust quality control
- Creative Domains: Benefit from human-in-the-loop collaboration
- Technical Domains (Development, Research): Heavily depend on tool integration
Architectural Evolution Path
- Start: Simple conversational agents with memory
- Add: Tool integration for external capabilities
- Scale: Multi-agent coordination for complex tasks
- Productionize: Quality control and deployment automation
🚀 Implementation Guidance
Choosing Your Architecture
| If Your Use Case Is… | Start With… | Scale To… |
|---|---|---|
| Customer interaction | Conversational + Memory | + Tool Integration |
| Data processing | Tool-Using + ReAct | + Workflow Orchestration |
| Complex research | Research + Planning | + Multi-Agent Coordination |
| Business automation | Workflow + Human-in-Loop | + Quality Control |
| Multi-domain tasks | Supervisor Architecture | + Swarm Collaboration |
Production Readiness Checklist
- ✅ Quality Control mechanisms
- ✅ Human-in-the-Loop where needed
- ✅ Scalability planning
- ✅ Appropriate deployment model
- ✅ Tool integration security
- ✅ Memory and context management
📚 Sources & References
This knowledge graph is synthesized from:
- LangGraph Official Documentation and templates
- Production Case Studies (LinkedIn, Uber, Replit, Elastic, Klarna)
- Open Source Implementations (Open Deep Research, Ollama Deep Research)
- Community Projects (Awesome LangGraph, 766+ stars)
- Research Papers and agentic AI literature
This knowledge graph provides a structured foundation for understanding agentic systems and designing appropriate solutions for specific domains and requirements.
