Agentic Software System
(Redirected from Software agent)
Jump to navigation
Jump to search
An Agentic Software System is an autonomous goal-directed AI-based software system that can support agentic task executions.
- AKA: AI Agent System, Agent-based System, Autonomous AI System, AI Agent-based Software System, Software Agent System, Agentic AI System, Digital Agent, Virtual Agent, AI-based Agent System.
- Context:
- Agentic Task Input: Agentic System Task, Agentic System Instructions, Agentic Environmental Data
- Agentic Task Output: Agentic System Actions, Agentic Task Results, Agentic System States
- Agentic Task Performance Measure: Agentic System Metrics such as agentic processing efficiency, agentic system effectiveness, and agentic task completion rate
- ...
- It can typically operate with or without explicit Agentic System Goals through agentic behavior patterns.
- It can typically implement Agentic System Architecture with agentic perception modules, agentic decision making modules, and agentic action modules.
- It can typically maintain Agentic System Knowledge Base with agentic domain knowledge, agentic task knowledge, and agentic learning history.
- It can typically process Agentic Environmental Inputs through agentic sensor interfaces and agentic data processing pipelines.
- It can typically generate Agentic System Responses via agentic action selection and agentic execution modules.
- It can typically utilize Agentic Learning Mechanisms for agentic performance improvement and agentic behavioral adaptation.
- It can typically exhibit Agentic System Autonomy through agentic self-directed operation and agentic independent decision-making.
- It can typically demonstrate Agentic Problem-Solving Capability through agentic strategy formulation and agentic solution exploration.
- It can typically support Agentic Human Interaction through agentic natural language interfaces and agentic context understanding.
- ...
- It can often employ Agentic Coordination Protocols for complex agentic task management.
- It can often implement Agentic Communication Protocols for inter-agentic system interaction.
- It can often use Agentic Monitoring Systems for agentic performance tracking and agentic behavioral analysis.
- It can often integrate Agentic Safety Mechanisms for agentic risk management and agentic error prevention.
- It can often support Agentic System Customization through agentic configuration interfaces and agentic parameter adjustments.
- It can often be developed using an Agentic System Development Environment such as LangChain, AutoGPT, or CrewAI.
- It can often leverage Agentic Tool Integration for agentic capability extension through agentic API integration and agentic plugin systems.
- It can often provide Agentic Explainability Features through agentic reasoning traces and agentic decision justifications.
- ...
- It can range from being a Goal-Free Agentic Software System to being a Goal-Directed Agentic Software System, depending on its agentic goal dependency.
- It can range from being a Simple Agentic Software System to being an Advanced Agentic Software System, depending on its agentic system sophistication.
- It can range from being a Centralized Agentic Software System to being a Distributed Agentic Software System, depending on its agentic architectural pattern.
- It can range from being a Simple-Task Agentic Software System to being a Complex-Task Agentic Software System, depending on its agentic task complexity.
- It can range from being a Specialized Agentic Software System to being a General-Purpose Agentic Software System, depending on its agentic capability scope.
- It can range from being a Human-Operated Agentic Software System to being a Self-Governing Agentic Software System, depending on its agentic autonomy level.
- It can range from being a Static-Knowledge Agentic Software System to being an Evolving-Knowledge Agentic Software System, depending on its agentic learning capability.
- It can range from being a Single-Domain Agentic Software System to being a Cross-Domain Agentic Software System, depending on its agentic domain coverage.
- It can range from being a Reactive Agentic Software System to being a Proactive Agentic Software System, depending on its agentic behavioral pattern.
- It can range from being a Single-Goal Agentic Software System to being a Multi-Goal Agentic Software System, depending on its agentic goal complexity.
- It can range from being a Supervised Learning Agentic Software System to being a Self-Learning Agentic Software System, based on its agentic learning autonomy.
- It can range from being a Single Agent-based Agentic Software System to being a Multi Agent-based Agentic Software System, depending on its agentic system composition.
- It can range from being a Deterministic Agentic Software System to being a Stochastic Agentic Software System, depending on its agentic decision model.
- It can range from being a Local Agentic Software System to being a Cloud-based Agentic Software System, depending on its agentic deployment environment.
- ...
- It can integrate with Agentic Development Frameworks for agentic system construction.
- It can utilize Agentic Orchestration Platforms for agentic workflow management.
- It can leverage Agentic Monitoring Infrastructure for agentic performance optimization.
- ...
- Example(s):
- Agentic AI Assistant Systems, such as:
- Conversational Agentic AI Assistants, such as:
- Claude (AI Assistant) for agentic conversational interaction and agentic task assistance.
- ChatGPT for agentic dialogue management and agentic content generation.
- Google Bard for agentic information synthesis and agentic query response.
- Microsoft Copilot for agentic productivity enhancement across Microsoft ecosystems.
- Specialized Domain Agentic AI Assistants, such as:
- Enterprise Agentic AI Assistants, such as:
- Conversational Agentic AI Assistants, such as:
- Agentic System Architecture Types, such as:
- Reactive Agentic Software Systems, such as:
- Deliberative Agentic Software Systems, such as:
- Agentic System Interaction Patterns, such as:
- Single Agentic Software Systems, such as:
- Multi-Agent Agentic Software Systems, such as:
- Domain-Specific Agentic Software Systems, such as:
- Industrial Agentic Software Systems, such as:
- Financial Agentic Software Systems, such as:
- Healthcare Agentic Software Systems, such as:
- Scientific Agentic Software Systems, such as:
- AI Agent-Powered Workflows, such as:
- Agent-based Intelligence Systems, such as:
- Agent-based Collaborative Systems, such as:
- Agent-based Multi-Agent Learning Systems, such as:
- Agent-based Coordination Systems, such as:
- Domain-Specific Agent-based Systems, such as:
- Agent-based Business Systems, such as:
- Agent-based Scientific Systems, such as:
- Agent-based Industrial Systems, such as:
- Advanced Agent-based Application Systems, such as:
- Agent-based Content-Centric Systems, such as:
- Agent-based Knowledge-Centric Systems, such as:
- Agentic Development Platforms, such as:
- ...
- Agentic AI Assistant Systems, such as:
- Counter-Example(s):
- Rule-Based Software System, which operates on predefined rules without agentic learning capability or agentic goal adaptation.
- Workflow Engine-based Software System, which executes predetermined workflows without agentic decision autonomy.
- Batch Processing System, which processes data batches without agentic environmental awareness or agentic adaptive behavior.
- Human Assistant, which provides human-based support rather than agentic software-based automation.
- Scripted Automation System, which follows rigid scripts without agentic situational adaptation.
- See: Software Agent, Agent Architecture, Multi-Agent System, Agent Communication, Agent Learning System, Autonomous System, Intelligent System, Agent-based Tightly-coupled System, Agent-based Loosely-coupled System, Agent-based Complex System, Agent-based Multi-Agent System, Agent-based Distributed System, Agent Coordination, Agent-based Swarm Intelligence, AI Agent Development Environment, AI Agent Communication Protocol, Domain-Specific AI Agent-based System, Interactive AI Agent-based System.
References
2024-11-24
- Perplexity.ai
- Question: What are agent-based software systems? Please explain their architecture, components, and applications. Include how these systems use agent communication protocols, resource management, and coordinated behaviors to maintain system dynamics and adaptability.
- Answer: Agent-based software systems are complex computational models that utilize multiple interacting software agents to achieve system-wide goals. These systems are characterized by their ability to simulate and solve complex problems through the collective behavior of autonomous agents.
- System Architectures:
- Design Types:
- Centralized vs Distributed: Systems may have a central controlling agent or operate in a fully distributed manner
- Static vs Dynamic: The number and properties of agents can be fixed or change over time
- Homogeneous vs Heterogeneous: Agents may have identical capabilities or diverse specializations
- Design Types:
- Communication and Interaction:
- Protocols:
- Resource Management:
- Resource allocation mechanisms
- Resource sharing protocols
- Distribution systems for resources
- Coordinated Behaviors:
- Rules and algorithms for collective action
- Protocols for team coordination
- Applications:
- Optimization and Problem-Solving:
- Swarm Intelligence for complex optimization
- Multi-Agent Learning for collaborative solutions
- Distributed search capabilities
- Simulation and Modeling:
- Social Networks modeling
- Ecosystems simulation
- Economic Systems modeling
- Robotics and Physical Systems:
- Business and Organizational Management:
- Optimization and Problem-Solving:
- System Advantages:
- Emergent Behaviors:
- Capture complex patterns from simple agent interactions
- Adaptability:
- Scalable and modifiable for changing requirements
- Scenario Testing:
- Controlled environment for scenario exploration
- Emergent Behaviors:
- Modeling Techniques:
- Rule-Based Modeling:
- Predetermined rules for behavior
- Interaction rules definition
- Learning Algorithms:
- Machine learning integration
- Reinforcement learning for adaptation
- Network Modeling:
- Network representation of agents
- Complex relationship modeling
- Rule-Based Modeling:
- System Architectures:
- Citations:
[1] https://en.wikipedia.org/wiki/Agent-based_model [2] https://botpress.com/blog/real-world-applications-of-ai-agents [3] https://www.mdpi.com/2078-2489/11/5/271 [4] https://builtin.com/articles/agent-based-modeling
2023
- GBard
- An agent is a system that can perceive its environment, make decisions and take actions to achieve a specific goal or set of goals. The agent operates autonomously, meaning it is not directly controlled by a human operator.
- The four rules that all AI agents must adhere to are:
- Agent Perception: The agent must be able to perceive the environment. This means it must have sensors that can collect information about the environment, such as cameras, microphones, and sensors that measure temperature, humidity, and light.
- Agent Decision-making: The agent must be able to make decisions based on the information it perceives. This means it must have a decision-making algorithm that can weigh the pros and cons of different actions and choose the one that is most likely to achieve the goal.
- Agent Action: The agent must be able to take actions to change the environment. This means it must have actuators that can perform actions, such as moving its limbs, driving a car, or controlling a robot.
- Agent Rational action: The agent's actions must be rational. This means they must be based on the agent's understanding of the environment and its goals.
- In addition to these four rules, there are other characteristics that are often associated with intelligent agents. These include:
- Agent Adaptability: The ability to learn and adapt to changes in the environment.
- Agent Sociability: The ability to interact with other agents and learn from them.
- Agent Creativity: The ability to generate new ideas and solutions to problems.
- The concept of an intelligent agent is a broad one, and there are many different types of agents that have been developed. Some examples of intelligent agents include:
- Game playing agents: These agents are designed to play games against humans or other agents.
- Search agents: These agents are designed to find information or objects in a large search space.
- Planning agents: These agents are designed to plan sequences of actions to achieve a goal.
- Robotic agents: These agents are designed to control robots and other physical systems.
- Virtual agents: These agents are designed to interact with humans in a virtual environment.
2022
- (Sutton, 2022) => Richard S. Sutton. (2022). “The Quest for a Common Model of the Intelligent Decision Maker". In: Proceedings of the Fifth Multi-disciplinary Conference on Reinforcement Learning and Decision Making
- ABSTRACT: The premise of the Multi-disciplinary Conference on Reinforcement Learning and Decision Making is that multiple disciplines share an interest in goal-directed decision making over time. The idea of this paper is to sharpen and deepen this premise by proposing a perspective on the decision maker that is substantive and widely held across psychology, artificial intelligence, economics, control theory, and neuroscience, which I call the "common model of the intelligent agent". The common model does not include anything specific to any organism, world, or application domain. The common model does include aspects of the decision maker's interaction with its world (there must be input and output, and a goal) and internal components of the decision maker (for perception, decision-making, internal evaluation, and a world model). I identify these aspects and components, note that they are given different names in different disciplines but refer essentially to the same ideas, and discuss the challenges and benefits of devising a neutral terminology that can be used across disciplines. It is time to recognize and build on the convergence of multiple diverse disciplines on a substantive common model of the intelligent agent.