A Practical Guide to Getting Started with Agents

Everyone is talking about AI. Every company, every tech person. It's nonstop! Based on the news you'd think it's already revolutionized the world, causing massive job displacement as artificial general intelligence (AGI) has already arrived. Based on my recent experience, while these models are really powerful and smart, we still have a long way to go. AI and AGI discussions are so wide ranging and cover tons of categories and use cases. One area I'm so excited about is called AI Agents, so this blog will focus on that.

AI agents, in particular, are emerging as a powerful design pattern that can automate complex tasks, improve accuracy, help us with analysis at scale and ultimately free up human workers for more strategic and creative activities. This guide explores some of the most impactful use cases for AI agents in my recent experience. My goal of this blog is to give you an idea of where to get started. Lastly, AI Agents can have so many definitions. For our purposes today, we are referring to a prompt or set of prompts, sent to an LLM in a loop with the ability to select tools (ie. software programs) to solve for specific sub-tasks when they need to. While admittedly very basic, I found this is conceptually the best place to start. Let's begin!

Understanding AI Agents in Back Office Operations

Before diving into specific use cases, it's important to understand what we mean by AI agents in the back office context. These are basically prompts in a loop using a Large Language Model (LLM). Some popular examples are ChatGPT from OpenAI and Claude from Anthropic but there are literally thousands today to choose from. These AI Agents are just software programs that can perform tasks traditionally handled by human workers. Today, they mostly loop through a process to get to an outcome to surface an exception. Soon, they will learn from their experience and operate with varying degrees of autonomy. Unlike previous automation tools, AI agents can handle the grey areas that required a level of basic human reasoning or thought. While they aren't great at this yet, most researchers think it's just a matter or more time and compute to get there. With that context on AI Agents, let's continue to the use cases.

Top Back Office Use Cases for AI Agents

1. Financial Operations and Accounting

Financial operations represent one of the most promising areas for AI agent deployment in my experience. These agents can transform how organizations handle their financial processes by:

- Automating accounts payable and receivable processes

- Reconciling accounts across multiple systems

- Detecting and flagging unusual transactions

- Processing expense reports and identifying policy violations

- Generating financial reports and analytics

The impact is particularly significant in reducing processing time and errors while improving compliance and audit trails. I've never met an underworked finance person. Working in Sales for most of my career has shown me that the finance team is one of the most important orgs in the company to have the most accurate timely data and that's where AI Agents can help.

2. Human Resources

HR departments can benefit significantly from AI agents handling routine tasks such as:

- Resume screening and initial candidate evaluation

- Employee onboarding documentation processing

- Benefits administration and enrollment

- Leave management and time tracking

- HR policy question handling and documentation

This allows HR professionals to focus on strategic activities like talent development and employee engagement. Onboarding flow are so complex and involve many parties. Coordinating all these parties at scale is extremely time consuming. That is where I've seen AI Agents able to help.

3. Data Management and Analysis

AI agents excel at understanding all types of data, making them ideal for:

- Data synthesis and standardization

- Cross-system data reconciliation

- Regular report generation and distribution

- Data quality monitoring and pattern detection

- Automated data entry and validation

While one of the most powerful areas longer term for AI Agents, a small caveat on this one to keep in mind, without proper data governance, quality and relationship modeling you can get yourself in a lot of trouble here with providing too much autonomy too soon. It's also best to avoid calculations for now, while some models are getting better this is a solved problem for decades in software, just use tools and function calling to save time and money.

4. Customer Service Support

While not traditionally considered "back office," internal customer service operations can be enhanced through AI agents that handle:

- Tier 1 support ticket classification and routing

- Standard inquiry responses and documentation

- Service level agreement compliance over time

- Knowledge base maintenance and updates

- Customer interaction analysis and reporting

To summarize, AI Agents are an incredibly powerful design pattern when working with AI models. A small caveat on all of these to keep in mind, without proper data governance, quality and relationship modeling you can get yourself in a lot of trouble fast with providing too much autonomy too soon. In my next post, we'll talk about how to get started and selecting your first AI Agent use case.

Disclaimer: The views, thoughts, and opinions expressed in this blog post are solely my own and do not represent those of any previous employer, client, or business partner. Any information shared is based on my personal experience and should not be construed as professional advice or official statements from any organization I have been affiliated with.