AI agents, also known as intelligent agents, are software programs designed to perceive their environment, take actions, and achieve specific goals autonomously. They differ from traditional computer programs in their ability to learn and adapt, make decisions, interact with surroundings, and operate with limited supervision. Agentic AI systems integrate one or more AI agents that collaborate with each other and provide a unified seamless experience and outcome for the end user.
AI agents and chatbots share similarities in their ability to interact with users, but differ significantly in their capabilities and underlying technologies:
Chatbots | AI Agents | |
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Focus | ||
Predefined tasks, scripted responses, and simple interactions | Complex tasks, dynamic decision-making, and collaboration with other agents | |
Technology | ||
Often rule-based, relying on keyword matching and pre-programmed responses | Built on AI techniques like machine learning, natural language processing, natural language understanding, knowledge representation, and basic levels of causal reasoning | |
Capabilities | ||
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Example | ||
An example of a chatbot is that of a customer service bot that uses a predefined script, provides basic information, and directs you to appropriate resources based on predefined options. | An example of an AI Agent is a highly trained personal assistant who can understand your needs, learn your preferences, and take initiative in helping you achieve your goals. |
In essence, AI agents are like general-purpose tools that can be adapted to various tasks requiring intelligence, decision-making, and collaboration. Chatbots, in contrast, are like specialized tools that excel at specific, well-defined tasks but lack the flexibility and adaptability of AI agents.
AI agent frameworks are a combination of libraries and workflows that facilitate the creation and management of intelligent software agents. Following are some of the latest frameworks to build AI agents:
AutoGen from Microsoft provides a multi-agent conversation framework as a high-level abstraction. It is an open-source library for enabling next-generation LLM applications where users can build LLM workflows with multi-agent collaborations and personalization. The agent modularity and conversation-based programming simplify development and enable reuse for developers.
Use case: An enterprise knowledge management system with a conversational interface using a knowledge base agent, retrieval agent, and dialogue agent.
CrewAI is an open-source framework built on top of LangChain for creating and managing collaborative AI agents. It enables developers to build cohorts of specialized AI agents that can work together to achieve complex tasks.
Use Case: A marketing team could use CrewAI to create a series of agents – one to gather customer data from social media, another to analyze sentiment, and a third to generate targeted marketing campaigns based on the insights.
LangGraph is also another open-source framework built on top of LangChain. It helps represent multiple agents in a graph network and ensures seamless integration and collaboration.
Use Case: An AI Research assistant that comprises a research content web scraping agent, a processing agent that identifies relevant content default behavior and synthesizes and stores curated content, and a generating agent that crafts initial drafts of research papers based on user goals and objectives
Despite the significant advancements in Agentic AI, there are several key challenges that still need to be addressed, such as:
As agentic AI systems get mainstream with their ability to accomplish complex goals, there is a need for a robust governance framework to overcome the challenges. A recent paper from OpenAI titled “Practices for Governing Agentic AI Systems” outlines some guidelines for safe and responsible development and deployment of such systems. Following are some key insights from the paper that would enable the responsible development and adoption of such systems:
It’s important to note that OpenAI’s framework is just a starting point, and ongoing research and discussion are crucial in developing comprehensive and effective governance models for agentic AI systems.
Real-world systems involve amalgamation of multiple capabilities, which warrants the design of Agentic AI systems that use multiple AI agents. We are seeing the emergence of such design patterns, given the limitations of large language models (LLMs) in producing outputs with just one API call for complex tasks. Since the quality of output of a system is multiplicative of the individual output quality from each subsystem, each subsystem would need its own output verification, validation, and feedback loop to ensure reliable and trustworthy outcomes. Governance of agentic AI is an ongoing process that requires continuous adaptation and improvement as the technology evolves.
By fostering collaboration, promoting transparency, and prioritizing ethical considerations, we can navigate the development and deployment of agentic AI responsibly and reap its benefits for the betterment of society. Agentic AI systems offer immense potential and are going to be a game changer in the coming days.
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