How to Get Started with AI Agents: A Beginner’s Guide

Looking to cut through the buzz around AI agents and cut to the chase of creating your own? Getting started with AI agents is easier than it may seem, but there’s still a lot to know before bringing AI agents to work on enterprise processes.

In this beginner's guide, we'll provide you with a comprehensive overview of what's inside an AI agent and how they can best be applied to enterprise workflows so you can get started with confidence.

If you're still getting familiar with the basics of AI agents, be sure to check out the first blog in this series, What Are AI Agents? (And Why Is Everyone Talking About Them). It covers the core concepts you need to know before diving deeper into how AI agents can transform your enterprise.

The benefits of AI agents are now within reach for enterprises

The concept of AI agents matured over many years of interconnected advances in AI technology. But the tipping point that made AI agents a sought-after tool for enterprise automation was the rapid evolution of generative AI—specifically, the proliferation of powerful large language models (LLMs). AI agents are a new and powerful design pattern in which AI can be used to expand the possibilities of automation in any enterprise.

Harnessing generative AI, AI agents are fast becoming must-haves for enterprises across industries to realize transformation goals and reimagine processes. With AI agents, organizations can combine the benefits of automation with the intelligence of generative AI to speed up critical processes, reduce human error, drive operational efficiency, and scale up process capacity on demand.

AI agents, for example, can improve customer experiences with expert-level, personalized support in minutes. In fact, we've already seen AI agents accelerate resolution by up to 50% in customer service applications.

Ready to get started?

First, what do you want to accomplish?

AI agents should be built for specific objectives and are most powerful when orchestrated as part of an end-to-end process involving other agents, automation steps, and human interaction. Consider a few examples of business processes that could be transformed by incorporating AI agents:

Service operations

Accelerate service effectiveness: Consider having AI agents integrated into your service processes to triage work, provide product knowledge, process return requests, and more.

Sales

Identify opportunities to upsell existing customers: An AI agent could be integrated into your customer journeys to provide personalized product recommendations, enhancing cross-selling and upselling opportunities.

Speed responses from your service desk: As with customer service, your service desk support flows can integrate agents to quickly resolve customer requests for information or even update applications and access.

Finance & accounting

Reconcile accounts faster: Create AI agents to accurately capture, standardize, and centralize transactions in real time across departments.

Healthcare

Reduce physician burnout: Alleviate administrative burden on physicians with AI agents to assist with routine yet complex work such as documentation processes.

AI agents allow for the automation of cognitive tasks that have typically required human work, such as reviewing a document, looking up information, and deciding on follow-up actions like raising an alert or updating a system.

How to employ AI agents today

Pairing AI with action, AI agents offer exciting opportunities when integrated with Intelligent Automation. AI agents embedded within and orchestrated as part of wider end-to-end process automations is where the real potential of this technology lies. Sub-steps of complete automations can be delegated to AI agents to complete specific tasks like looking up information from a knowledge base or analyzing information before taking an action like initiating a product return.

Giving AI agents a clear and narrow scope of objectives is a powerful way to harness their value while ensuring sufficient guardrails on their behavior and outputs to minimize risk within a business process. For example, an AI agent could provide solutions to low-risk customer requests, such as requests for information, while categorizing and handing off more complex or sensitive customer inquiries for action/approval by a human.

Understanding the workflow of AI agents

AI agents follow a structured workflow that can be broken down into several key stages. Let’s explore each step:

1. Intake

AI agents start with inputs from upstream steps in a process, events from systems, or other user inputs like direct prompts. This initial step sets the stage for defining clear objectives by synthesizing perceived data to establish goals and understand what needs to be accomplished.

2. Understanding

Once the inputs are captured, AI agents process the information. Using techniques like retrieval augmented generation (RAG), it looks up relevant data to aid in decision-making. This ensures the agent is well-informed and can respond accordingly.

3. Planning next steps

With relevant information in hand, the agent decides the best course of action. It can either proceed with a response or plan additional steps within the workflow.

4. Action

Action and execution are typically next, where the agent can use tools such as automations leveraging APIs or UI actions. Building agents capable of solving complex workflows requires access to a range of these tools that can interact across a company’s enterprise landscape.

5. Reflection and validation

Before completing its work, an agent often has a reflection step, which can validate whether what has been done meets the defined objectives and, if not, whether it should iterate the process again or ask a person for additional clarification.

By capturing inputs, outputs, and intermediate steps from agent executions, agents can improve over time by adjusting strategies and behaviors for enhanced efficiency, effectiveness, and overall performance.

Example: AI agent in customer support

Taking an AI agent in customer support as an example, that workflow might look something like:

Receive customer inquiry and analyze to understand the type of request and any additional relevant details, such as customer sentiment.
Identify related knowledge, inquiry data, and order tracking/product inventory in company systems.
Choose an appropriate next step based on the type of inquiry and related information.
Respond to the customer with follow-up questions or information, or if necessary, transfer the inquiry to be handled by a human.
Review the context of the original request against the response and decide if any additional work is needed to successfully fulfill the objectives.

For a comprehensive look at how AI agents can optimize service operations, explore our Service Ops AI Agent Guide.

So, what are AI agents made of?

To harness the potential of AI agents within enterprise operations, let’s take a closer look at the key components that constitute AI agents and drive their functionality:

Engine: LLM/gen AI model

At the core of an AI agent is its engine—typically advanced language models like large language models (LLMs) or generative AI models. These engines empower AI agents to comprehend unstructured content and generate human-like text, enabling them to process complex information and interact in natural language. The choice of a foundational model will impact the cost, output accuracy, and overall performance of the AI agent.

Prompts: AI skills

AI skills leverage optimized prompts (templates) to complete a particular task according to specifically defined guidelines. For example, a prompt may be used to determine whether an input request meets certain policy guidelines for escalating work to particular expert groups in an organization.

Data: Inputs + context

AI agents live and breathe by the data they receive and have access to, encompassing inputs from users, external data sources, and the contextual information that shapes model responses. Analyzing and understanding all available data is what enables AI agents to respond with relevance and accuracy. RAG is a commonly used technique with agents because it provides a powerful search capability to augment the inputs to models to get the most relevant results. At the same time, data privacy is a significant factor to consider in terms of the information AI agents receive and have access to use. Anonymization and data masking are important processing steps where workflows involve sensitive information.

Actions: Tool access + APIs

The action component of AI agents encompasses the tools, APIs, and access points that enable them to perform tasks and execute commands. The breadth of these capabilities is a key driver of the overall potential of AI agents because, without extensive connectivity across an enterprise, agents are often relegated to simple informational tasks. However, when using AI agents in an enterprise, they must be set up within a robust security framework to establish access controls as well as guardrails to keep agent actions within the bounds of compliance regulations and company standards.

Human collaboration and governance

Ensuring output quality, as well as the integrity and reliability of AI agents, requires incorporating human-in-the-loop mechanisms for assistance, approvals, and oversight. Robust governance practices and visibility into AI model interactions are also necessary to monitor for responsible use and performance.

Not a data scientist or pro developer? Don’t worry.

Creating custom AI agents is quickly becoming faster, easier, and accessible without data science or even developer expertise. In the early days of LLMs and AI agents, the only options to build them were directly in code, and many component parts required net new development, like function calling or RAG. Models were also less predictable and powerful, so agents were more brittle and unpredictable in their behavior.

However, today, new no-code tools are quickly transforming the landscape. These tools bring agent creation to business users with pre-built model connectors, native RAG, built-in safety guardrails, and the ability to easily interact with the agent through out-of-the-box UI elements.

As emphasized here, AI agents can be even more valuable when working as a team in coordination/sequence to automate entire end-to-end business processes. Orchestrating AI agents is already available within advanced AI and automation enterprise systems, paving the way for agentic automation of complex processes that underpin the most critical outcomes companies are trying to achieve, such as improved customer experience.

Getting started

At Automation Anywhere, as the pace of AI innovation accelerates, we are focused on empowering organizations to safely and effectively harness the potential of AI agents. That's why many of our latest advancements involve putting AI agents to work within an enterprise context.

Whether you're an AI and automation veteran or just starting out, our AI + automation enterprise system is built to make AI more accessible with practical tools to build AI agents and integrate them confidently into your most important enterprise processes.

Ready to see AI agents in action? Start building your own solutions with our how-to guides, or experience them firsthand in a live demo.