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Also known as: Intelligent Agent, Representatives, Delegates
An AI system that perceives, plans, and acts to achieve goals.
DESCRIPTION
Agents sense their environment, reason about goals, and select actions to maximize utility or task success. They can be reactive, deliberative, or hybrid, and may use planning or learning. In modern LLM-based systems, agents orchestrate tool calls, memory, and iterative reasoning to complete complex tasks. Agents range from simple rule-based bots to sophisticated autonomous systems with long-term objectives. Agent architectures often define perception, decision-making, and actuation components. Performance depends on reward design, feedback signals, and environment observability. Safety and alignment considerations are central when agents act with real-world impact.
ALTERNATIVE DEFINITIONS
Wikipedia
In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators and directs its activity towards achieving goals.
Stresses autonomy, sensing, acting, and goal-directed behavior.
Artificial Intelligence: A Modern Approach
An agent perceives its environment and takes actions that maximize its expected performance measure.
Focuses on performance measures and rational action under uncertainty.
Legal Information Institute (Cornell Law School)
An agent is someone who has the authority to act on behalf of another (the principal).
Legal framing centered on authority and representation.
Cambridge Dictionary
a person who acts for or represents another
Plain-language definition of representation.
RELATED TERMS
Autonomous Agent Planning Perception Actuator Environment Policy Reinforcement Learning Belief-Desire-Intention Function Calling Multi-Agent System Agency Broker Power of Attorney Contract Authorization Delegation Consent Identity Access Control
COMPETING TERMS
Robotic Process Automation Principals
RELATIONS
- Equivalent to: Autonomous Agent
- Narrower: Agentic AI Autonomous Agent Open Policy Agent Software Agent
KNOWLEDGE AREAS
AI Data Science Domain & Business Context Data Management Data Privacy & Compliance
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager Information Analyst Data Steward Data Architect Data Governance Manager Chief Data Officer Data Management Consultant
EXTERNAL SOURCES
- Wikipedia - Intelligent agent
- Artificial Intelligence: A Modern Approach - Canonical textbook reference
- OpenAI Spinning Up - RL primer on agents/environments
- Anthropic - Overview of agentic patterns
- Legal Information Institute (Cornell Law School) - Agency law overview
- UK Government - Power of attorney guidance
TOP QUESTIONS
What defines an Agent in AI?
An Agent perceives an environment, reasons over goals, and acts to maximize success. The perception-action loop is fundamental to the definition.
Category: Definition
How do Agents differ from simple scripts?
Agents decide based on context and goals, not fixed steps. They adapt to observations and feedback, unlike rigid scripts.
Category: Comparison
Why are Agents popular with LLMs?
LLMs can reason, plan, and call tools, enabling agent loops. This makes complex, multi-step tasks achievable with fewer hand-crafted rules.
Category: Relevancy
What are common Agent architectures?
Reactive, deliberative, and hybrid architectures are common, often enhanced with planning, memory, and tool-use modules.
Category: Architecture
What risks come with deploying Agents?
Unintended actions, reward hacking, and security vulnerabilities can occur. Guardrails, policy checks, and monitoring are essential.
Category: Risk
How are Agents evaluated?
Use task success rate, sample efficiency, safety metrics, and cost/latency KPIs. Benchmarks and human evaluation provide complementary perspectives.
Category: Evaluation
What is the role of planning in Agents?
Planning helps Agents compute action sequences toward goals, improving reliability on long-horizon tasks.
Category: Concept
Where should I start learning about Agents?
Begin with AIMA’s agent chapters, RL primers like Spinning Up, and agentic LLM framework tutorials.
Category: Learning
How do multi-agent systems relate to a single Agent?
Multi-agent systems coordinate multiple agents with potentially competing goals; a single agent focuses on its local objectives.
Category: Relationship