Agent-based System

An AI Agent-based System is a software-based agent system that is an AI-based system (for performing agent-based AI tasks).

• AKA: AI Agent System, Intelligent Agent Platform.
• Context:
  • Input(s): agent task, agent instructions, environmental data.
    • optional: agent parameters, task constraints.
  • Output(s): agent actions, task results.
  • Performance Measure(s): task success rate, agent efficiency, learning progress.
  • ...
  • It can range from being a Simple AI Agent System to being a Complex AI Agent System, depending on its agent complexity.
  • It can range from being a Single AI Agent System to being a Multi-Agent AI System, depending on its agent coordination scope.
  • It can range from being a Reactive AI Agent System to being a Planning AI Agent System, depending on its agent reasoning type.
  • It can range from being a Fixed AI Agent System to being an Adaptive AI Agent System, depending on its learning capability.
  • It can range from being a Limited-Task AI Agent System to being a General-Task AI Agent System, depending on its task scope.
  • It can range from being a Human-Guided AI Agent System to being an Autonomous AI Agent System, depending on its autonomy level.
  • ...
  • It can perform Agent Task Execution.
  • It can learn from Task Experience.
  • It can adapt to Environmental Changes.
  • It can interact with System Users.
  • It can coordinate with Other Agents.
  • ...
• Examples:
  • By Agent Architecture Type, such as:
    • Single-Agent Architectures, such as:
      • Task-Specific Agent Systems, for focused operation.
      • General Agent Systems, for diverse tasks.
    • Multi-Agent Architectures, such as:
      • Collaborative Agent Systems, for team tasks.
      • Distributed Agent Systems, for parallel processing.
  • By Agent Learning Type, such as:
    • Supervised Agent Systems, such as:
      • Guided Learning Systems, for structured learning.
      • Feedback-Based Systems, for performance improvement.
    • Reinforcement Learning Systems, such as:
      • Exploration Agent Systems, for policy learning.
      • Optimization Agent Systems, for behavior refinement.
  • By Agent Application Type, such as:
    • Task Automation Systems, such as:
      • Process Agent Systems, for workflow automation.
      • Decision Agent Systems, for choice selection.
    • Assistant Agent Systems, such as:
      • Support Agent Systems, for user assistance.
      • Service Agent Systems, for task completion.
  • ...
• Counter-Examples:
  • Non-AI Agent System.
  • Traditional Software System.
  • Manual Task System.
• See: AI Agent, Agent Architecture, Learning System, Task Automation, Multi-Agent System.

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