AI Agents: Features and Uses Beyond Traditional Chatbots

AI Agents: Features and Uses Beyond Traditional Chatbots (59)

Estimated reading time: 7 minutes

Key Takeaways

  • An AI agent is a software system that can reason, plan, and act to achieve set goals for users using AI, external tools, memory, and iterative cycles (source).
  • Unlike traditional chatbots, AI agents can interact directly with systems, APIs, and software, not just chat (reference).
  • They offer abilities like task planning, breaking down objectives, using external tools, and have contextual memory across several iterations (details).
  • Each AI agent can adapt its actions based on intermediate results and context, unlike traditional automation (IBM).
  • Current use cases include customer service, internal process automation, employee support, workflow integration, and data analysis (see examples).
  • The most widely-accepted definition today is that AI agents are generative, LLM- or AI-based systems, operating in a cycle of observation → reasoning → action (Hugging Face Guide).

Table of Contents

What is an AI Agent?

An AI agent is an autonomous software system that uses artificial intelligence to sense its environment, reason about its next actions, execute them, and reach predefined goals. Unlike other AI systems, agents can dynamically decide the flow of their actions, using external tools, memory, and repeatedly iterating until the job is done (Google Cloud, IBM).

There are various industry and academic definitions of AI agents. IBM, for example, describes an agent as a system that performs tasks autonomously through flexible orchestration of workflows with tools (see reference), while Amazon Web Services focuses on the use of observations and target-driven actions.

Key Features of an AI Agent

  • Reasoning and real-time decision-making, planning and adjusting actions as it works (LangChain).
  • Contextual memory: can remember prior states across a session or multiple steps (AWS).
  • Uses tools: has access to APIs, databases, external systems and business software (Hugging Face).
  • Autonomous iteration: can repeat, self-correct, and adjust the plan until the objective is met (Zendesk).
  • Feedback loop: the agent reviews intermediate results and adjusts actions as needed (IBM).

Most modern AI agents are powered by large language models (LLMs), which make dynamic decisions about the control flow in your applications (LangChain).

How AI Agents Differ from Chatbots and Traditional Automation

  • Classic chatbots are limited to generating responses, either rule-based or via conversational AI. AI agents can execute actions on software, services, business systems, or APIs in addition to talking (Hugging Face).
  • Traditional automation runs routine processes using fixed rules and workflows. An AI agent is constantly adapting to the current context and the outcome of each step, optimizing the path to your goal (IBM).

At its core, an AI agent is both autonomous and adaptive. It is related to chatbots and automation, but goes far beyond both.

Main Types of AI Agents

According to IBM (source), AI theory recognizes five classic types:

  • Simple reflex agents
  • Model-based reflex agents
  • Goal-based agents
  • Utility-based agents
  • Learning agents

Today, in 2024, most “modern” agents are LLM-based systems that work through an iterative cycle: observe → reason → use tools → execute → re-evaluate (LangChain).

Current Uses for AI Agents

  • Customer service and issue resolution: AI agents can handle entire customer conversations and fully resolve cases (AWS).
  • Employee support: managing HR and IT support tickets (LangChain).
  • Business process automation: from employee onboarding and approvals to routing and complex cases (IBM).
  • Data gathering and analysis: searching, enriching, sorting, and analyzing large volumes of information (AWS).
  • Internal operations: triggering actions within corporate systems, running automated workflows, and much more (LangChain).

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Frequently Asked Questions

  • How is an AI agent different from a chatbot?
    A chatbot generates replies or conversations. An AI agent also makes decisions, executes actions on software, uses external tools, maintains context across multiple steps, and adapts to the outcome of every action (see reference).
  • What types of tasks can an AI agent handle?
    Task planning, process automation, internal support, data collection and analysis, direct API interactions, business systems management, and much more (AWS).
  • Does every AI agent rely on a large language model (LLM)?
    Not always. While most advanced examples now use LLMs for reasoning and decision-making, there are still AI agents based on rules, traditional logic, and classical machine learning (McKinsey).
  • Are there risks or limitations when using AI agents?
    Yes: potential bias in decision-making, errors when interacting with external systems, possible security issues, and model training dependency are all factors to consider (IBM).