The promise of cloud is agility, but the reality of cloud migration often looks more like a high-stakes, one-time project. When faced with sprawling, complex legacy applications — particularly in Java or .NET — the traditional “lift-and-shift” approach is only a halfway measure. It moves the complexity, but doesn’t solve it. The next strategic imperative for the CIO is to transition from periodic, costly overhauls to continuous modernization powered by autonomous agentic AI. This shift transforms migration from a finite, risk-laden project into an always-on optimization engine that continuously grooms your application portfolio, directly addressing complexity and accelerating speed-to-market.****

The autonomous engine: Agentic AI for systematic refactoring

Agentic AI systems are fundamentally different from traditional scripts; they are goal-driven and capable of planning, acting and learning. When applied to application modernization, they can operate directly on legacy codebases to prepare them for a cloud-native future.

Intelligent code refactoring

The most significant bottleneck in modernization is refactoring — restructuring existing code without changing its external behavior to improve maintainability, efficiency and cloud-readiness. McKinsey estimates that Generative AI can shave 20–30% off refactoring time and can reduce migration costs by up to 40%. Agentic AI tools leverage large language models (LLMs) to ingest entire repositories, analyze cross-file dependencies and propose or even execute complex refactoring moves, such as breaking a monolith into microservices. For applications running on legacy Java or .NET frameworks, these agents can systematically:

• Identify and flag “code smells” (duplicated logic, deeply nested code).
• Automatically convert aging APIs to cloud-native or serverless patterns.
• Draft and apply migration snippets to move core functions to managed cloud services.

Automated application dependency mapping

Before any refactoring can begin, you need a complete and accurate map of application dependencies, which is nearly impossible to maintain manually in a large enterprise. Agentic AI excels at this through autonomous discovery. Agents analyze runtime telemetry, network traffic and static code to create a real-time, high-fidelity map of the application portfolio. As BCG highlights, applying AI to core platform processes helps to reduce human error and can accelerate business processes by 30% to 50%. In this context, the agent is continuously identifying potential service boundaries, optimizing data flow and recommending the most logical containerization or serverless targets for each component.

Practical use cases for continuous value

This agentic approach delivers tangible business value by automating the most time-consuming and error-prone phases of modernization:

Use Case	AI Agent Action	Business Impact
Dependency mapping	Analyzes legacy code and runtime data to map component-to-component connections and external service calls.	Reduced risk: Eliminates manual discovery errors that cause production outages during cutover.
Intelligent code refactoring	Systematically restructures code for cloud-native consumption (e.g., converting monolithic C# or Java code into microservices).	Cost & speed: Reduces developer toil and cuts transformation timelines by as much as 50%.
Continuous security posture enforcement	The agent autonomously scans for new vulnerabilities (CVEs), identifies affected code components and instantly applies security patches or configuration changes (e.g., updating a policy or library version) across the entire portfolio.	Enhanced resilience: Drastically reduces the “time-to-remediation” from weeks to minutes, proactively preventing security breaches and enforcing a compliant posture 24/7.
Real-time performance tuning	Monitors live workload patterns (e.g., CPU, latency, concurrent users) and automatically adjusts cloud resources (e.g., rightsizing instances, optimizing database indices, adjusting serverless concurrency limits) to prevent performance degradation.	Maximized ROI: Ensures applications are always running with the optimal balance of speed and cost, eliminating waste from over-provisioning and avoiding customer-impacting performance slowdowns.

Integrating human-in-the-loop (HITL) framework governance

The transition to an agent-driven modernization model doesn’t seek to remove the human role; rather, it elevates it from manual, repetitive toil to strategic governance. The success of continuous modernization hinges on a robust human-in-the-loop (HITL) framework. This framework mandates that while the agent autonomously identifies optimization opportunities (e.g., a component generating high costs) and formulates a refactoring plan, the deployment is always gated by strict human oversight. The role of the developer shifts to defining the rules, validating the agent’s proposed changes through automated testing and ultimately approving the production deployment incrementally. This governance ensures that the self-optimizing environment remains resilient and adheres to crucial business objectives for performance and compliance.

Transforming the modernization cost model

The agentic approach fundamentally transforms the economic framework for managing IT assets. Traditional “lift-and-shift” and periodic overhauls are viewed as massive, high-stakes capital expenditure (CapEx) projects. By shifting to an autonomous, continuous modernization engine, the financial model transitions to a predictable, utility-like pperational expenditure (OpEx). This means costs are tied directly to the value delivered and consumption efficiency, as the agent continuously grooms the portfolio to optimize for cost. This allows IT to fund modernization as an always-on optimization function, making the management of the cloud estate a sustainable, predictable line item rather than a perpetual budget shock.

Shifting the development paradigm: From coder to orchestrator

The organizational impact of agentic AI is as critical as the technical one. By offloading the constant work of identifying technical debt, tracking dependencies and executing routine refactoring or patching, the agent frees engineers from being primarily coders and maintainers. The human role evolves into the AI orchestrator or System Architect. Developers become responsible for defining the high-level goals, reviewing the agent’s generated plans and code for architectural integrity and focusing their time on innovation, complex feature development and designing the governance framework itself. This strategic shift not only reduces developer burnout and increases overall productivity but is also key to attracting and retaining top-tier engineering talent, positioning IT as a center for strategic design rather than just a maintenance shop.

The pilot mandate: Starting small, scaling quickly

For CIOs facing pressure to demonstrate AI value responsibly, the adoption of agentic modernization must begin with a targeted, low-risk pilot. The objective is to select a high-value application—ideally, a non-critical helper application or an internal-facing microservice that has a quantifiable amount of technical debt and clear performance or cost metrics. The goal of this pilot is to prove the agent’s ability to execute the full modernization loop autonomously: Discovery > Refactoring > Automated Testing > Human Approval > Incremental Deployment. Once key success metrics (such as a 40% reduction in time-to-patch or a 15% improvement in cost efficiency) are validated in this controlled environment, the organization gains the confidence and blueprint needed to scale the agent framework horizontally across the rest of the application portfolio, minimizing enterprise risk.

The strategic mandate: Self-optimizing resilience

By adopting autonomous agents, the operational model shifts from reactive fixes to a resilient, self-optimizing environment. Gartner projects that autonomous AI agents will be one of the fastest transformations in enterprise technology, with a major emphasis on their ability to orchestrate entire workflows across the application migration and modernization lifecycle. These agents are not just tools; they are continuous improvement loops that proactively:

• Identify a component that is generating high cloud costs.
• Formulate a refactoring plan for optimization (e.g., move to a managed serverless queue).
• Execute the refactoring, run automated tests and deploy the change incrementally, all under strict human oversight.

The CIO’s task is to define the strategic goals — cost, performance, resilience — and deploy the agents with the governance and human-in-the-loop controls necessary to allow them to act. This proactive, agent-driven model is the only path to truly continuous modernization, ensuring your cloud estate remains an agile asset, not a perpetual liability

This article is published as part of the Foundry Expert Contributor Network. Want to join?

SUBSCRIBE TO OUR NEWSLETTER

From our editors straight to your inbox

Get started by entering your email address below.