Understanding the Basics of Agentic AI and Application Modernization

Application modernization, the process of updating older software and legacy systems, is usually a heavy lift for most organizations and can take several months, or even years. Legacy applications and monolithic systems are complex, intertwined with other applications and often written in outdated programming languages, making them difficult to update. Yet, they often remain mission-critical to keep business running as usual.

Despite the complexity around application modernization, it is a necessary initiative to improve performance and unlock digital transformation. Simply retiring and replacing an outdated application isn’t always a viable solution and can result in a bad return on investment. To help optimize application modernization, agentic AI can be a useful tool.

Agentic AI refers to artificial intelligence systems designed to act autonomously, making decisions and performing tasks with minimal human intervention. It goes beyond generative AI, which is focused on producing content, providing answers to questions and/or creating text, images, videos and music, based on existing data and in reaction to a human prompt. 

Agentic AI on the other hand can take action with minimum human input and focuses on achieving predefined objectives. For example, an AI agent can be designed to search for flights, compare prices and make reservations for the best option. Agentic AI can adapt, learn and execute complex workflows, and often uses generative AI to complete its tasks. 

In the context of application modernization, agentic AI can assist organizations in analyzing legacy systems, generating test cases, refactoring code and even automating parts of the development process.

Here’s how agentic AI is reshaping the application modernization process:

Legacy Code Analysis and Documentation
Many organizations struggle with undocumented legacy systems, especially when the original developers are no longer on staff to help provide historical knowledge. Agentic AI can analyze these "black box" applications, generate documentation and provide insights into their functionality. This is particularly valuable for businesses looking to modernize without starting from scratch.

Automated Testing and Quality Assurance
One benefit of Agentic AI is its ability to create and execute test cases. By aligning test cases with business requirements, organizations can ensure that their modernized applications meet expectations and provide reliable results.

Code Refactoring and Optimization
While Agentic AI can assist with minor code modifications, it’s not yet reliable for large-scale refactoring of enterprise applications.  However, for smaller and less complex systems, it can greatly accelerate the modernization process.

Skill Gap Bridging
Agentic AI can help developers understand unfamiliar programming languages or legacy codebases, effectively closing skill gaps. For example, a developer unfamiliar with common business-oriented language (COBOL) can use agentic AI to interpret and update COBOL applications.

Agentic AI is not a magic wand; it requires careful governance, clear business requirements and robust testing to ensure success. Here are three key considerations for organizations embarking on their app mod journey with agentic AI.

1. AI Governance and Security
Organizations must establish clear AI policies to ensure data security and compliance. Leaking source code to external AI models can pose significant risks, especially with frontier models like Claude or Grok. This can happen because agentic systems have memory, can send data to external APIs or other tools and create logs of actions taken across workflows. 

It’s important to mask or exclude sensitive code and data, monitor what the agent accesses and sends, and limit file access. It is better to assign the least amount of privileges to an AI agent and require more human interaction instead of a hands-off approach that results in compliance violations, data breeches and compromised agents. 

2. Cost
The cost of using Agentic AI can be prohibitive for large-scale applications, as pricing is often based on token usage. Businesses must carefully evaluate the ROI before using agentic AI for extensive modernization projects.

Keep in mind that agentic AI should not be a self-managed tool. Don’t expect to use it and instantly achieve high savings in labor costs. Human intervention is required to ensure quality and security standards. Agentic AI may accelerate application modernization but it won’t eliminate every roadblock on its own.

3. Quality Assurance
Blindly trusting AI-generated code can lead to unreliable outputs that fail to meet business needs. Robust testing and human oversight are essential to ensure quality.

Because agents can act, they may refactor, patch or deploy code and push unreviewed changes to production. To avoid this, it is important to require human approval for certain actions. You should also track what the agent accesses and sends, set up alerts for unusual behavior and whitelist trusted tools to avoid leaking sensitive information to public repositories. 

While agentic AI may not be able to complete your application modernization to-do list on its own, it serves as a valuable tool that can springboard digital transformation by automating tedious tasks, bridging skill gaps and accelerating development timelines.

To ensure success with agentic AI, it is important to start with a clear strategy that defines your business requirements and AI governance framework before diving in. Begin with smaller, less complex applications to build confidence, demonstrate value and ensure accuracy. Use test-driven development to ensure that AI outputs are reliable and align with business needs.

If your organization does not have the internal resources necessary to successfully and efficiently complete an application modernization project, consider partnering with a trusted advisor, such as CDW, to get the technical expertise and insight you need. Even if you do have a robust development team, partnering with an agnostic third-party like CDW can lead to faster, better results.  

CDW's Strategic Application Modernization Assessment (SAMA) uses automation to evaluate applications across 26 dimensions — such as complexity and security — within two to three weeks, identifying technical debt, risks and dependencies. It equips organizations with a detailed modernization roadmap, helping leaders prioritize apps based on business impact, complexity and security needs. With SAMA, you don’t need to worry about setting up an agent or risk your sensitive information.