AI Agent-Based Workflow

An AI Agent-Based Workflow is an AI-driven workflow that leverages AI agents (to enable autonomous processing and agent coordination).

• AKA: Agent-Driven Workflow, Multi-Agent Process Flow, Agent-Orchestrated Workflow.
• Context:
  • Task Input: Agent Configuration, Task Objectives, Coordination Rules.
    • Optional Input: Inter-Agent Protocols, Agent Constraints, Communication Patterns.
  • Task Output: Agent Actions, Interaction Logs, Performance Metrics.
  • Task Performance Measure: Agent Efficiency, Coordination Quality, Task Completion Rate.
  • ...
  • It can typically orchestrate Agent Interaction through coordination engine.
  • It can typically maintain Agent State through state management system.
  • It can typically control Agent Communication through message broker.
  • It can typically manage Agent Memory through knowledge base.
  • It can typically ensure Agent Safety through constraint system.
  • It can typically monitor Agent Behavior through observation system.
  • It can typically handle Agent Failure through recovery mechanism.
  • ...
  • It can range from being a Single-Agent Workflow to being a Multi-Agent Workflow, depending on its agent complexity.
  • It can range from being a Specialized Agent Workflow to being a General Agent Workflow, depending on its task scope.
  • It can range from being a Sequential Agent Workflow to being a Parallel Agent Workflow, depending on its execution pattern.
  • It can range from being a Supervised Agent Workflow to being an Autonomous Agent Workflow, depending on its human oversight.
  • It can range from being a Simple Agent Workflow to being a Complex Agent Workflow, depending on its interaction complexity.
  • ...
  • It can integrate with Task Management System for work coordination.
  • It can connect to Knowledge Base for information access.
  • It can interface with Monitoring System for performance tracking.
  • It can support Communication Platform for agent messaging.
  • It can maintain Audit System for interaction logging.
  • ...
• Examples:
  • Agent Collaboration Workflows, such as:
    • Task Distribution Workflows, such as:
      • Task Assignment Workflow for workload distribution.
      • Resource Allocation Workflow for resource optimization.
      • Priority Management Workflow for task prioritization.
    • Team Coordination Workflows, such as:
      • Consensus Building Workflow for decision alignment.
      • Conflict Resolution Workflow for disagreement handling.
      • Goal Synchronization Workflow for objective coordination.
  • Specialized Agent Workflows, such as:
    • Research Agent Workflows, such as:
      • Information Gathering Workflow for data collection.
      • Analysis Workflow for insight generation.
      • Report Generation Workflow for finding synthesis.
    • Support Agent Workflows, such as:
      • Query Resolution Workflow for question answering.
      • Issue Handling Workflow for problem solving.
      • Follow-up Workflow for interaction completion.
  • Enterprise Agent Workflows, such as:
    • Business Process Workflows, such as:
      • Sales Agent Workflow for customer interaction.
      • HR Agent Workflow for employee support.
      • Support Agent Workflow for service delivery.
  • ...
• Counter-Examples:
  • Single Model Workflow, which lacks agent autonomy and interactive behavior.
  • Static Rule Workflow, which misses adaptive decision making and dynamic coordination.
  • Manual Coordination Workflow, which requires human orchestration rather than agent self-organization.
  • Pipeline Workflow, which follows fixed sequences rather than dynamic interactions.
• See: Agent System, Workflow Orchestration, Multi-Agent Coordination, Agent Communication, Task Distribution, Performance Monitoring.

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