AI Agent-Supported Workflow
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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
- 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 Function Category Workflows:
- Agent-Supported Industry Category Workflows:
- Agent-Supported Risk Category Workflows:
- 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.
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.
- AI Agents:
- Design Patterns:
- Task Decomposition:
- 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.
- Enhanced Efficiency:
- Key Components:
- 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