AI Agent-Supported Workflow

An AI Agent-Supported Workflow is an automated workflow that relies on AI agents.

• Context:
  • Process Input: AI Agent-Supported Process Definition, AI Agent-Supported Rules, AI Agent-Supported Logic
    • Optional Input: AI Agent-Supported Assignment, AI Agent-Supported Conditions, AI Agent-Supported Notifications
  • Process Output: AI Agent-Supported Results, AI Agent-Supported Logs, AI Agent-Supported Statistics
  • Process Performance Measure: AI Agent-Supported Completion, AI Agent-Supported Timing, AI Agent-Supported Utilization
  • ...
  • It can (typically) execute AI Agent-Supported Step through autonomous agent system.
  • It can (typically) manage AI Agent-Supported Assignment through agent routing.
  • It can (typically) enforce AI Agent-Supported Rule through agent logic.
  • It can (typically) track AI Agent-Supported State through agent monitoring.
  • It can (typically) coordinate AI Agent-Supported Resource through agent allocation.
  • It can (often) support AI Agent-Supported Process through agent orchestration.
  • It can (often) handle AI Agent-Supported Exception through agent recovery.
  • It can (often) enable AI Agent-Supported Logic through agent configuration.
  • It can (often) maintain AI Agent-Supported Audit through agent logging.
  • It can (often) facilitate AI Agent-Supported Coordination through agent management.
  • ...
  • It can range from being a Simple AI Agent-Supported Workflow to being a Complex AI Agent-Supported Workflow, depending on its process complexity.
  • It can range from being a Single-Agent AI Agent-Supported Workflow to being a Multi-Agent AI Agent-Supported Workflow, depending on its agent architecture.
  • It can range from being a Specialized AI Agent-Supported Workflow to being a General-Purpose AI Agent-Supported Workflow, depending on its application scope.
  • It can range from being a Reactive AI Agent-Supported Workflow to being a Proactive AI Agent-Supported Workflow, depending on its decision autonomy.
  • It can range from being a Domain-Specific AI Agent-Supported Workflow to being a Open-Domain AI Agent-Supported Workflow, depending on its ....
  • ...
  • It can integrate with AI Agent-Supported Business System for agent automation.
  • It can connect to AI Agent-Supported Learning System for agent improvement.
  • It can support AI Agent-Supported Analytics for agent monitoring.
  • It can interface with AI Agent-Supported Directory for agent access.
  • ...
• Example(s):
  • Agent-Supported Intelligence Workflow Categorys:
    • Agent-Supported Basic Task Workflows:
      • Agent-Supported Customer Query Workflows composed of customer query analysis agents and customer response generation agents.
      • Agent-Supported Recruitment Workflows composed of candidate resume analysis agents and candidate evaluation agents.
    • Agent-Supported Process Optimization Workflows:
      • Agent-Supported Resource Allocation Workflows composed of resource analysis agents and efficiency optimization agents.
      • Agent-Supported Logistics Workflows composed of logistics route planning agents and logistics delivery tracking agents.
    • Agent-Supported Knowledge Work Workflows:
      • Agent-Supported Contract Review Workflows composed of legal document analysis agents and compliance verification agents.
      • Agent-Supported Software Documentation Workflows composed of software code analysis agents and software documentation generation agents.
    • Agent-Supported Decision Support Workflows:
      • Agent-Supported Risk Assessment Workflows composed of risk analysis agents and risk evaluation agents.
      • Agent-Supported Strategy Planning Workflows composed of strategy analysis agents and decision optimization agents.
  • Agent-Supported Function Category Workflows:
    • Agent-Supported Analysis Workflows:
      • Agent-Supported Code Review Workflows composed of code quality analysis agents and improvement recommendation agents.
      • Agent-Supported Case Research Workflows composed of legal precedent analysis agents and case relevance assessment agents.
    • Agent-Supported Generation Workflows:
      • Agent-Supported Document Creation Workflows composed of content analysis agents and document generation agents.
      • Agent-Supported Course Creation Workflows composed of curriculum design agents and learning content generation agents.
    • Agent-Supported Monitoring Workflows:
      • Agent-Supported Patient Care Workflows composed of patient health monitoring agents and patient alert generation agents.
      • Agent-Supported Transaction Workflows composed of financial transaction monitoring agents and fraud detection agents.
    • Agent-Supported Service Workflows:
      • Agent-Supported Customer Support Workflows composed of customer ticket routing agents and customer issue resolution agents.
      • Agent-Supported Onboarding Workflows composed of employee task management agents and employee progress tracking agents.
  • Agent-Supported Industry Category Workflows:
    • Agent-Supported Business Operations Workflows:
      • Agent-Supported Customer Service Workflows composed of customer engagement agents and service optimization agents.
      • Agent-Supported Supply Chain Workflows composed of inventory prediction agents and inventory optimization agents.
    • Agent-Supported Technical Domain Workflows:
      • Agent-Supported Architecture Design Workflows composed of system requirement analysis agents and architecture pattern recommendation agents.
      • Agent-Supported Code Debug Workflows composed of software error analysis agents and software solution recommendation agents.
    • Agent-Supported Professional Services Workflows:
      • Agent-Supported Legal Service Workflows composed of legal document analysis agents and compliance verification agents.
      • Agent-Supported Healthcare Service Workflows composed of patient health monitoring agents and medication adherence monitoring agents.
  • Agent-Supported Risk Category Workflows:
    • Agent-Supported Low-Stakes Workflows:
      • Agent-Supported Meeting Scheduling Workflows composed of calendar analysis agents and schedule optimization agents.
      • Agent-Supported Social Media Workflows composed of content moderation agents and engagement tracking agents.
    • Agent-Supported High-Stakes Workflows:
      • Agent-Supported Emergency Response Workflows composed of critical incident analysis agents and response coordination agents.
      • Agent-Supported Financial Trading Workflows composed of market analysis agents and risk mitigation agents.
• Counter-Examples:
  • Workflow Engine-based Workflow, which follows predefined paths without autonomous decision.
  • Static Automation Workflow, which lacks adaptive capability and learning mechanism.
  • Rule-Based Workflow, which operates on fixed logic without dynamic adaptation.
  • Manual Process Workflow, which requires human decision rather than agent autonomy.
  • Single-Purpose Workflow, which performs specific tasks without intelligent orchestration.
• See: Automated Workflow, AI Agent-Supported Process, AI Agent-Supported Routing, AI Agent-Supported Rule, AI Agent-Supported Orchestration, AI Agent-Supported Monitoring.

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References

2024-12-05

• Perplexity.ai
  • Question: Explain how agentic workflows differ from workflow engine-driven workflows, including their key components, advantages, and real-world applications. What makes them unique in terms of AI agent collaboration, task decomposition, and decision-making capabilities? How do they enhance business process automation and operational efficiency?
  • Answer: Agentic workflows represent an advanced approach to AI-powered automation that fundamentally transforms how complex tasks are executed. Unlike traditional automation, these workflows enable AI agents to make independent decisions, adapt to changing conditions, and collaborate to achieve specific goals.
    • Key Components:
      • AI Agents:
        • Function as sophisticated instances of large language models.
        • Make autonomous decisions and adapt to new information.
        • Collaborate in multi-agent systems to handle complex tasks.
      • Process Architecture:
        • Breaks down complex tasks into manageable subtasks.
        • Implements continuous feedback and improvement loops.
        • Enables real-time adaptation and optimization.
    • Design Patterns:
      • Task Decomposition:
        • Strategic planning and goal setting.
        • Subtask delegation among specialized agents.
        • Continuous monitoring and adjustment.
      • Multi-Agent Collaboration:
        • Agents work together with defined roles and responsibilities.
        • Share information and coordinate actions.
        • Combine specialized capabilities for comprehensive solutions.
    • Business Impact:
      • Enhanced Efficiency:
        • 41% higher accuracy compared to traditional methods.
        • Continuous learning and optimization.
        • Real-time adaptation to changing conditions.
  • Citations:

[1] https://promptengineering.org/exploring-agentic-wagentic-workflows-the-power-of-ai-agent-collaborationorkflows-the-power-of-ai-agent-collaboration/
[2] https://beam.ai/agentic-workflows
[3] https://www.tapclicks.com/blog/what-is-agentic-workflow-and-how-does-it-work
[4] https://www.ampcome.com/post/agentic-workflow-all-you-need-to-know-about-building-ai-agents
[5] https://www.alvarezandmarsal.com/insights/ready-ai-automation-use-large-language-model-agentic-workflow-power-your-business
[6] https://zhukov.live/understanding-ai-agentic-workflows-a-comprehensive-guide-002c95c05e2a?gi=cf23bb51a513
[7] https://www.multimodal.dev/post/ai-agentic-workflows
[8] https://www.miquido.com/ai-glossary/ai-agentic-workflows/
[9] https://www.vellum.ai/blog/agentic-workflows-emerging-architectures-and-design-patterns
[10] https://www.xenonstack.com/blog/agentic-workflows

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