How to Build an AI Agent for Real-World Applications

This comprehensive guide explains how to build AI agents for real-world applications and how to incorporate them into your business.

AI has quickly moved from a buzzword to a critical component of business strategy. Central to this strategy are AI agents, which can solve complex problems autonomously or in cooperation with human counterparts. In this blog, we aim to help you gain a deeper understanding of AI agents, determine whether you need an AI agent framework, and outline how to build an AI agent that meets your organizational needs.

Defining Agents and Their Trajectory

The emergence of ChatGPT in late 2022 marked a significant shift in the business landscape, with generative AI quickly becoming a tool for enhancing productivity across industries. Our recent survey of business leaders revealed a diverse approach to AI adoption, with Microsoft Copilot leading, followed by individual use of consumer AI versions like ChatGPT.

The same survey showed that 92 percent of respondents associate AI agents with chatbots, virtual assistants, robotic process automation (RPA), or “something else.” While there are some similarities, AI agents are large language models. They’re set apart from these more traditional AI technologies because they can operate independently and use tools to make informed decisions without human intervention. They can engage with their environment, use designated tools, and improve performance based on past interactions.

Understanding and implementing AI agents can transform operational efficiency, customer engagement, and data-driven decision-making. But there’s a caveat. The most successful organizations understand their needs, objectives, appetite for change, and long-term AI strategy.

Use cases span industries from insurance to financial services, healthcare, retail, manufacturing, and more. In fact, according to CIO, NASA’s Jet Propulsion Laboratory is already using agents to ensure flight hardware is not contaminated.

Insurance companies could use agents to associate inbound documents with their claims systems. Energy and utilities companies could process and analyze utility bills, implement safety compliance scoring for incident reports, or handle customer questions and complaints with automated responses.

Healthcare organizations and financial institutions can do many of the same things to streamline processes and workflows and improve efficiency in their sectors. Industries like manufacturing, retail, and others could use AI agents for many manual processes.

While the notion of AI agents isn’t new, they’re now within reach of organizations, more autonomous, and more efficient than ever. Here’s how to get started planning your AI agent.

Getting Started with Building AI Agents

Step 1: Define Your Objectives

Implementing an AI agent or a multiagent system isn’t the right choice for every organization. It may seem basic, but the first step in building an AI agent is clearly defining your objectives. Ask yourself:

• What specific problem are you trying to solve?
• What are the key performance indicators for success?
• How will the AI agent integrate with existing systems and workflows?

Having clear answers to these questions will guide your development process and help ensure your AI agent delivers tangible value to your organization.

Step 2: Assess Organizational Readiness

Before diving into the technical aspects of building an AI agent, organizational leaders must first assess their organization’s readiness.

Start by establishing strong governance and the necessary technology infrastructure to support AI initiatives. Ensure your data is secure, accessible, and of high quality while also prioritizing privacy. Implement responsible AI practices to mitigate risks, such as bias or unintended consequences, and maintain transparency in decision-making.

Leaders should also evaluate their organizational culture and overall support for change initiatives to help understand potential roadblocks and shape implementation.

Step 3: Navigating the Build Decision

When preparing for AI agent development, organizations face a critical choice: use existing frameworks or build custom solutions. Each offers its own set of advantages and challenges.

Preexisting Frameworks

Preexisting AI agent frameworks like AutoGen, LangChain, and CrewAI can offer a great way to get started.These frameworks typically enable a faster time-to-market, allowing organizations to implement and start reaping the benefits of their AI solutions more quickly. They often come with lower initial development costs, as much of the foundational work has already been done.

However, they may force you into a fixed reasoning process, and some don’t let you reason at all. For example, each of the above preexisting frameworks relies on predefined workflows or templates making them excel in certain areas, but lag in others. LangChain can chain AI models and APIs, but it doesn’t support complex reasoning without custom logic. AutoGen can streamline development, but it doesn’t have context-aware reasoning abilities. Similarly, CrewAI requires customization for advanced reasoning capabilities.

Organizational leaders could face limitations in customization and control, which could be problematic for businesses with specific requirements or those operating in highly regulated industries.

And because they’re designed to be “all things to all people,” they often include features that may not be relevant to every project, leading to unnecessary overhead for simpler projects.

In addition, quality output is another significant consideration when using preexisting frameworks.  Some frameworks process prompts in ways that can lead to poor results, especially as you scale up your project. Since these frameworks often feed outputs back into inputs, low-quality results can create a cycle of increasingly inaccurate or hallucinated responses.

Given the limitations of preexisting frameworks, many organizations are turning to custom solutions, which can be customized to address the quality and scalability issues inherent in some off-the-shelf options.

Custom Frameworks

Custom solutions provide complete control over functionality and integration, allowing organizations to build AI agents that align perfectly with their existing systems and processes. You can optimize these for specific business needs, potentially leading to better performance and efficiency. In addition, they often allow for enhanced security and data privacy management, which is essential for organizations that work with sensitive data.

Because they must be built from the ground up, custom solutions typically have higher upfront development costs and take longer to deploy. Custom solutions also require specialized in-house expertise, which can be difficult and expensive to acquire and maintain.

Ultimately, your choice will depend on the complexity of your problem, available data, and how much adaptability and customization are needed. Once you’ve decided, you’re ready to give it the tools it needs to work.

Step 4: Arm Your Agent with Data and Tools

AI agents are only as good as the data they’re trained on and the tools they’re given to operate. When agents have access to API calls, access to the internet, your CRM or other databases, and even other AI models, they will perform better than those without those tools.

In AI agent development, clearly define what the agent should accomplish, what tools they have available and how to use them, and how to act from a role perspective. Giving an agent too many tasks or tools or not enough instruction will lead to confusion and produce less-than-optimal results.

Pay close attention to key parameters that impact creativity in your output and use the appropriate setting for your task. For example, if you’re extracting or looking up data, you’ll want to keep creativity low, but creativity should be dialed up if you’re generating creative content.

For data sources:

• Identify relevant data sources within your organization
• Ensure data quality through cleaning and preprocessing
• Consider privacy and security implications of data usage
• Develop a strategy for continuous data collection and updates

In addition, ensure your agent has a framework for making decisions and acting. Define its scope, outline how it makes decisions, create feedback loops for learning from outcomes, and establish safeguards for human oversight mechanisms. It’s important to give agents a way to move something to a human queue if they can’t complete the task.

A good rule of thumb is to treat your agent like a capable yet inexperienced intern, providing well-constructed instructions and prompts.

Once your AI agent is developed and tested, it’s time to move into the deployment phase.

Step 5: Deploy and Pilot Your AI Agent

Start by deploying your AI agent in a controlled environment, ensuring your team is well-trained on its functionality. If employees are expected to interact with the agent, provide thorough training on both its capabilities and limitations.

Once this foundation is established, pilot your agent in a small test group. Closely monitor performance and accuracy to ensure the agent meets expectations in real-world scenarios. This phase allows for proactively identifying and resolving issues before a broader rollout. After fine-tuning the agent, proceed with a full-scale implementation.

Step 5: Continuous Improvement

The work doesn’t stop when you deploy your AI agent. To ensure long-term success:

• Continually monitor performance
• Retrain models with new data
• Adapt to changing business and improvement needs

A phased approach minimizes risk and allows for ongoing improvements while maximizing the chances of a successful large-scale rollout.

Building an AI Agent is Worth the Effort

Building an AI agent is a complex and challenging endeavor. However, when it’s done with a focus on your business objectives, agents can drive significant value for your organization.

By embracing the challenge of building AI agents, you’re not only keeping pace with technological advancements. You’re positioning your organization at the forefront of innovation, ready to thrive in an increasingly AI-driven business landscape.

Are you ready to explore how artificial intelligence can fit into your business but aren’t sure where to start? Our AI experts can guide you through the entire process, from planning to implementation. Talk to an expert