Definition
AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt
Their capabilities are made possible due to the multimodal capacity of gen AI and AI foundation models. AI agents can process multimodal information like text, voice, video, audio, code and more simultaneously; converse, reason, learn, and make decisions
Key Features
- Reasoning: the core cognitive process involves using logic and available information to draw conclusions, make inferences, and solve problems
- Acting: The ability to take action or perform tasks based on decisions, plans, or external input is crucial for AI agents to interact with their environment and achieve goals.
- Observing: Gathering information about the environment or situation through perception or sensing is essential for AI agents to understand their context and make informed decisions
- Planning: Developing a strategic plan to achieve goals is a key aspect of intelligent behavior. AI agents with planning can id the necessary steps, eval potential actions and choose the best course of action based on avaliable info and desired outcomes.
- Collaborating: Working effectively with humans or AI agents to achieve a common goal. Collaboration requires communication, coordination and the ability to understand and respect the perspective of others
- Self-refining: The capacity for self-improvement and adaptation is a hallmark of advanced AI systems. AI agents with self-refining capabilities can learn from experience, adjust behavior based on feedback and continuously enhance their performance and capabilities over time.
What’s the difference between AI agents, AI assistance, and bots?
AI assistants are AI agents designed as applications or products to collaborate directly with users and perform tasks by understanding and responding to natural human language and inputs. They can reason and take action on the users’ behalf with their supervision
| Category | AI agent | AI assistant | Bot |
|---|---|---|---|
| Purpose | Autonomously and proactively perform tasks | Assisting users with tasks | Automating simple tasks or conversations |
| Capabilities | Can perform complex, multi-step actions; learns and adapts; can make decisions independently | Responds to requests or prompts; provides information and completes simple tasks; can recommend actions but the user decides | Follows pre-defined rules; limited learning; basic interactions |
| Interaction | Proactive; goal-oriented | Reactive; responds to user requests | Reactive; responds to triggers or commands |
How do AI agents work?
Every agent defines its role, personality, and communication style
- Persona: allows agent to maintain a consistent character and behave in an appropriate manner
- Memory: equipped with short term, long term, consensus, and episodic memory
- Tools: functions or external resources that an agent can utilize to interact with its environment and enhance its capabilities
- Model: LLMs act as the “brain” of an agent, enabling them to process and generate language, while other components facilitate reason and action
What are the types of agents in AI?
Based on interaction
how they interact with users…
- interactive partners: assisting users with tasks like customer service, healthcare, education and scientific discovery
- autonomous background processes: background agents. For automating routine tasks, analyze data for insights, optimize processes for efficiency, and proactively identify and address potential issues
Based on number of agents
- single agent: operate independently to achieve a specific goal
- multi-agent: collaborate or compete to achieve a common objective or individual goals
Challenges with using AI agents
- Tasks requiring deep empathy / emotional intelligence or requiring complex human interaction and social dynamics
- Situations with high ethical stakes
- Domains with unpredictable physical environments
- Resource-intensive applications