How Do AI Agents Work?
AI Agents combine user objectives with available tools to perform tasks. By sharing information between systems, and using memory and reasoning to provide comprehensive responses, AI agents are able to complete tasks of varying complexity.
The functionality of AI Agents can be broken into three pillars, each playing a critical role in their effectiveness.
Natural Language Processing
Reasoning and Decision Making
Response Generation, Automation, and Interaction
Natural Language Processing
AI Agents analyze user input using natural language processing. A typed command (i.e. utterance) can be transformed in a number of ways to ensure the communication is being understood. Techniques like tokenization, lemmatization, stemming, and named entity recognition (NER) break down the initial utterance into pieces that the agent can understand so that the next phase can begin.
Reasoning and Decision Making
In this stage, the course of action is determined. Initially, the system employs enumerative reasoning and a series of retrieval-augmented generation (RAG) techniques across structured and unstructured knowledge sources, automated workflows, and applications. Additionally, the system may utilize generative AI to provide predictions or craft solutions to fulfill the user's request effectively. This ‘knowledge chain’ effectively acts as the brain of the AI agent, interpreting the understood information and plotting a course of action.
Response Generation, Automation, and Interaction
The final stage involves crafting and delivering a response to the user. The output could include content generation, the execution of a task or action, a summary of results or steps performed, or even a recommendation.
These three components work together to create a comprehensive system capable of understanding, processing, and interacting with users in a meaningful way.