What Is Agentic Coding? How AI Agents Modernize Code

2026-06-08 · Source: IBM Technology · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

Agentic coding introduces AI systems that autonomously navigate, understand, and propose changes within codebases, significantly modernizing legacy applications. Studies indicate developers spend 60-70% of their time understanding existing systems, a challenge exacerbated by "lost understanding" due to tangled dependencies, framework gaps (e.g., Java 8 vs. Java 17), and undocumented external connections. An "AI development partner" acts as a "building inspector," performing investigative work to map call paths, data flows, and hidden dependencies. This enables an iterative modernization cycle: analyzing the codebase to surface unknown sequences (like credit checks in a specific order), identifying safe boundaries for logic separation, generating comprehensive test coverage, and allowing developers to review and approve changes. The new and old systems can run side-by-side, with AI flagging divergences. Crucially, human approval, full change history in Git, and no autonomous deployment ensure control and traceability, making it suitable for sensitive applications like financial services loan processing.

Key takeaway

For AI Engineers or Directors of AI/ML tasked with modernizing complex legacy applications, agentic coding offers a structured approach to overcome "lost understanding." You can deploy AI development partners to analyze codebases, identify safe refactoring boundaries, and generate robust test coverage, reducing the 60-70% of time typically spent on context. Ensure human approval, full change history, and no autonomous deployment are integrated to maintain control and traceability, especially in regulated environments like financial services.

Key insights

Agentic coding employs AI to autonomously analyze and evolve legacy code, drastically cutting developer time spent on system comprehension.

Principles

• Lost understanding, not old code, is the core modernization challenge.
• Iterative cycles with AI partners enable safe, controlled modernization.
• Human oversight is critical for AI-driven code changes in sensitive systems.

Method

An AI development partner analyzes the codebase, identifies safe change boundaries, generates test coverage, and runs new/old systems side-by-side, with human review at each step.

In practice

• Use AI to map hidden dependencies in legacy systems.
• Generate comprehensive tests for edge cases before refactoring.
• Implement human approval for all AI-proposed code changes.

Topics

• Agentic Coding
• Legacy Code Modernization
• AI Development Partners
• Codebase Analysis
• Software Refactoring
• Financial Services

Best for: Software Engineer, AI Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.