What are AI Agents?

In the effort to automate granular AI tasks, AI Agents are born. AI Agents try to meet predetermined goals set by humans. Agent will try to use all tools at its disposal to meet the goal including collecting data, prompt for additional information or interact with it’s environment.

Simple example

AI agent is tasked with resolving live customer support ticket. It can use combination of available documentation and queries to customer to try to determine the desired outcome. If it fails it can decide to pass the ticket to human.

How do AI Agents work?

At the core most AI Agents follow the basic cycle:

• Perception: They collect information for various given sources such as sensors, databases, scraping or user input
• Processing: AI Agent will analyze the data using algorithms often powered by machine learning such as LLMs.
• Decision Making: At this stage AI Agent needs to decide the best course of action
• Action: Execute the task decided, examples include sending alerts, automating workflows or interacting with user via some interface
• Learning: Use the actions taken and user feedback to learn, adopt and refine future runs.

Benefits of using AI Agents

• Productivity improvement: AI agents are self-sufficient intelligent systems designed to carry out specific tasks without human involvement. Businesses utilize AI agents to accomplish targeted objectives and drive more efficient outcomes. By offloading repetitive tasks to AI agents, teams can focus on high-priority or creative initiatives, ultimately bringing greater value to their organization.
• Cost reduction: Businesses can leverage intelligent agents to cut unnecessary costs caused by process inefficiencies, human errors, and manual workflows. With autonomous agents operating under a reliable and adaptable model, complex tasks can be executed with confidence, even in evolving environments.
• Decision making: Advanced intelligent agents leverage machine learning (ML) to collect and analyze vast amounts of real-time data. This empowers business managers to make faster, more informed decisions when planning their next steps. For instance, AI agents can be used to assess product demand across various market segments during an ad campaign.
• Improved customer experience: Customers expect engaging and personalized experiences when interacting with businesses. By integrating AI agents, companies can tailor product recommendations, deliver instant responses, and drive innovation to enhance customer engagement, boost conversions, and build lasting loyalty.

How to get started?

Most use cases fall under widely available and adapted solutions such as Amazon Lex, Rasa, HubSpot AI, etc.

For building a complex AI agent we recommend to start with LangChain and LangGraph, both powerful frameworks for building more complex agents.

At some point we will post an example here, so subscribe to get notified 🙂