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Also known as: Intelligent Agent
An autonomous software entity that perceives its environment and acts to achieve goals.
DESCRIPTION
A software agent continuously senses an environment, reasons or learns, and executes actions to pursue objectives. Intelligent agents encompass reactive, deliberative, and learning architectures, including reinforcement learning agents. In multi-agent systems, agents interact and coordinate or compete. Applications include assistants, trading bots, simulators, and robotics control software. Agent designs emphasize autonomy, feedback loops, and goal-oriented behavior distinct from simple scripts or services.
ALTERNATIVE DEFINITIONS
Wikipedia
An intelligent agent is an autonomous entity which observes through sensors and acts upon an environment using actuators and directs its activity towards achieving goals.
Highlights autonomy, sensing, acting, and goal pursuit.
Artificial Intelligence: A Modern Approach
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.
Provides the canonical agent definition used in AI curricula.
RELATED TERMS
Autonomous Agent Multi-Agent System Reinforcement Learning Perception Actuator Environment Belief–Desire–Intention Planner Policy
COMPETING TERMS
RELATIONS
- Broader: Agent
- Narrower: Agentic AI
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Software Developer AI Product Manager
EXTERNAL SOURCES
- Wikipedia - Intelligent agent
- Autonomous Agents and Multi-Agent Systems - Research on agent theory and practice
- OpenAI - Research on agents learning to act
TOP QUESTIONS
How does a Software Agent differ from a microservice?
Agents are autonomous, goal-directed, and environment-aware, while microservices expose stateless APIs within orchestrated systems. Agents emphasize perception–action loops.
Category: Alternatives
Where are Software Agents used today?
Digital assistants, trading systems, robotics, simulation, customer support, and web automation commonly deploy agent architectures.
Category: Application
What is the agent architecture loop?
Perceive, think/plan/learn, and act. This loop repeats continuously to adapt behavior to the environment.
Category: Basics
What’s a common mistake in building agents?
Hard-coding brittle rules without feedback mechanisms leads to poor robustness. Include learning or adaptation and clear reward signals.
Category: Mistakes
How do reinforcement learning and agents relate?
RL formalizes agents that learn policies to maximize cumulative reward by interacting with environments.
Category: Concept
What is a multi-agent system?
A collection of interacting agents that may cooperate or compete, requiring coordination and communication protocols.
Category: Basics
How are goals represented for Software Agents?
As reward functions, utility models, or symbolic goals in planners like BDI architectures.
Category: Theory
What are key evaluation metrics for agents?
Task success rate, cumulative reward, sample efficiency, robustness, and safety constraints are common metrics.
Category: Testing
What are risks of deploying autonomous agents?
Unintended actions, reward hacking, and safety violations. Careful reward design, constraints, and monitoring mitigate risks.
Category: Risk