Reimagining the application migration and modernization value-chain: The agentic way

In the past few years, AI-powered tools have been valuable allies in the application migration and modernization journey—streamlining well-defined, repeatable tasks that previously required significant human effort. From automating code scans and dependency mapping to accelerating test cycles and data migrations, traditional AI has brought speed, accuracy and consistency to numerous activities within the modernization value chain. These tasks include activities such as:

• Application discovery and analysis: Application infrastructure discovery, code analysis and complexity assessment, dependency mapping, cloud readiness assessments, technical debt analysis and risk scoring
• Migration design: Target technology stack, resource right-sizing and configuration, platform-tool fitment and so on.
• Code transformation assistance: Code translation, snippet generation, code conversion (for example, COBOL, .NET, Java™) to modern equivalents, syntax correction and optimization suggestions
• Automated testing: Test case generation, regression test automation, defect prediction and other testing capabilities
• Security and compliance scanning: Static code analysis for vulnerabilities, compliance mapping and other security-related assessments
• Observability and telemetrics: Progress tracking and dashboards, knowledge extraction and related reporting functions
• DevSecOps: Pipeline management, security checks, monitoring plus dynamic alerts for addressing failure points and code reviews
• SDLC process definition: Marking a transition from human-driven to AI-led SDLC process management

These capabilities have helped organizations compress timelines, reduce human error and achieve predictable outcomes, especially in areas where rules, patterns and objectives are well understood. Yet, while these AI assets excel at “known” problems, the evolving complexities of modern application landscapes demand an approach that is adaptive as well as autonomous, capable of addressing unexpected problems.

Agentic AI takes pushes AI capabilities far beyond the limits of standard AI. Where traditional AI thrives on predefined rules and datasets, agentic AI introduces autonomy, context-awareness and adaptive decision-making—enabling it to tackle ambiguous, evolving and multistep modernization challenges. These intelligent agents can explore codebases, infer architectural patterns, propose optimal modernization paths and dynamically adjust plans as new information emerges. 

Beyond analysis, agentic AI translates insights into action. It can orchestrate entire workflows across diverse tools, integrate with heterogeneous environments and even initiate corrective actions without waiting for human prompts. By blending the speed of automation with the agility of strategic reasoning, agentic AI transforms application migration and modernization from a series of isolated optimizations. It enables a coordinated, end-to-end evolution that is aligned with business outcomes.

• Acts autonomously to perceive, plan, act and learn across the end-to-end AMM lifecycle
• Can handle dynamic goals, reprioritize tasks and self-orchestrate modernization workflows without constant human input

• Orchestrates entire workflows across discovery, blueprinting, transformation and deployment
• Continuously adapts to evolving technical, operational and business contexts
• Can reason, adjust its plan and provide contextual insights in real time

• Enables "human-agent squads" where agents and humans collaborate actively
• Agents escalate exceptions, learn from human feedback and contribute to outcomes
• Enhances productivity while ensuring strategic alignment through human oversight

• Goes beyond task-level intelligence to deliver goal-directed behavior, cross-phase reasoning and dynamic learning
• Capable of handling complex decision paths and providing recommendations on modernization strategies (for example, choosing between 6 R paths, cloud architecture and operational requirements)

• Enables transformational value by streamlining the entire AMM value chain, accelerating delivery and reducing risk
• Shifts from static playbooks to self-orchestrating modernization systems with continuous improvement loops

• With the redefined SDLC process with agentic AI, teams unlock new levels of speed, intelligence and resilience in application modernization

The agentic approach highlights the difference from the traditional capabilities by actively executing individual tasks. It can also understand dependencies, evaluate tradeoffs and proactively sequence activities for maximum impact. For example, instead of merely flagging outdated components, agents can assess their business criticality, recommend modernization approaches, simulate potential outcomes and autonomously trigger migration or integration workflows. 

This shift from task execution to goal-oriented orchestration enables organizations to move from piecemeal modernization to a unified, strategic transformation—delivering greater speed, reduced risk and tangible business value.

In the agentic approach to application migration and modernization, the software development lifecycle shifts from being a series of isolated, manually coordinated steps to a fluid, intelligent and self-optimizing process. By combining reasoning capabilities with deep contextual knowledge, these agents remove inefficiencies, streamline decision-making and clear away unnecessary steps. This approach unlocks faster delivery, higher quality and modernization journeys that are both simpler and more strategically aligned to business goals. 

There are three key roles that agentic AI plays in the migration modernization processes: autonomous orchestrators, enablers and reinvention catalysts.

Agents take the lead in automating repetitive, low-value tasks—managing goals dynamically and adapting continuously to transformation needs.

Agents enhance human decision-making by evaluating modernization strategies, surfacing hidden complexities and collaborating on high-value tasks.

With reasoning and contextual awareness, agents eliminate non-essential activities and simplify convoluted migration paths—unlocking speed, clarity and scale.

The rise of agentic AI is giving birth to a new hybrid workforce, one that blends human expertise with intelligent AI agents into unified delivery squads. These AI-augmented teams operate within a “hybrid delivery” model, where agents bring scalable, centralized automation to handle repetitive, rules-based and high-volume tasks across modernization projects. 

By standardizing execution and enhancing productivity, these agents ensure that processes are performed with precision, consistency and speed—regardless of the size or complexity of the portfolio being modernized. 

This change in workforce structure also has a direct impact on the skill model, with human roles focused toward devising novel solutions, creative thinking and business alignment. Therefore, the skill pyramid shifts upwards with low value, repetitive and routine tasks taken away by agents.

For customers, this hybrid model translates into tangible benefits across journey acceleration, cost efficiency and risk reduction. Importantly, it creates a future-ready operating model that adapts to evolving business demands, helping customers achieve modernization goals with greater confidence, resilience and strategic advantage.

Agentic AI delivers a wide range of benefits that streamline and strengthen application migration and modernization efforts, including:

• Speed and agility: Agentic AI accelerates timelines by autonomously executing repetitive and low-value tasks while dynamically adapting to shifting project goals. This approach allows teams to respond quickly to new requirements or changing priorities, significantly reducing delays and compressing overall project delivery cycles.
• Improved accuracy and quality: By continuously assessing its own outputs and learning from errors, agentic AI ensures higher accuracy across the modernization lifecycle. 
• Enhanced decision-making: Agentic AI augments human judgment by evaluating multiple strategies in real time. It can identify hidden complexities—such as dependencies across multiple application stacks, legacy systems and target cloud environments—to present the optimal path forward based on performance, cost and risk.
• Better resource optimization: Through autonomous orchestration, agentic AI allocates both human and technical resources where they deliver the most value.
• Risk reduction: By proactively identifying bottlenecks, incompatibilities and migration risks early in the process, agentic AI reduces the likelihood of costly rework or postdeployment failures. 
• Continuous adaptation: Rather than operating with a fixed plan, agentic AI continually evaluates progress against defined goals and adapts workflows in real time. This approach ensures that modernization efforts remain aligned with evolving business objectives, technology changes and stakeholder needs without costly restarts.
• Simplified processes: Agentic AI uses reasoning capabilities and a robust knowledge base to streamline convoluted migration and modernization paths. It dramatically reduces the workstreams performed by humans and offloads the activities to AI. An example of such a transition can be seen in the picture below that depicts typical scenarios for migration of virtual machines. 

IBM’s approach to application migration and modernization with agentic AI starts with an interlock between the program objectives and realization approach. It then recommends a prescriptive approach to agentic application migration and modernization activities. The end phase outlines agent-optimized and managed steady-state operations.

The various phases of the IBM method and their value are shown in the figure and elaborated in the next sections.

This foundational phase sets the stage for agentic modernization. It is primarily human-led, focusing on aligning stakeholders, identifying operational bottlenecks and integrating client-specific AI platforms and agents. Key activities include defining the Minimum Viable Product (MVP), assessing security and compliance readiness and establishing the strategic and architectural baseline for agentic execution.

In this phase, AI agents take the lead in automating discovery across the IT landscape. They analyze application portfolios, map dependencies, assess cloud readiness and identify modernization opportunities. Agents also perform deep code analysis and technical debt assessment, while human stakeholders guide the vision and validate insights. This phase builds a comprehensive understanding of the current environment to inform downstream decisions.

Insights from exploration are transformed into actionable strategies. AI agents generate modernization paths (for example, 6 R analysis), create target state architecture blueprints and automate code transformation and infrastructure provisioning. They also support platform-tool fitment analysis and data lineage mapping. Human experts oversee and refine these outputs, ensuring alignment with business goals. Prioritizing applications into migration waves is a key outcome of this phase.

This phase operationalizes the transformation strategy through AI-driven execution. Agents provision infrastructure, automate data migration, optimize migration paths and deploy applications. They also generate and run test cases, detect defects and recommend remediations based on predictive insights. Security scans and policy enforcement are embedded into the process, ensuring compliance and quality at scale.

After successful deployment, the focus shifts to sustaining and enhancing value. AI agents continuously monitor application performance, reliability and security. They provide proactive insights for capacity planning, cost optimization and SLA adherence. Agents also enable real-time dashboards for business metrics and maintain audit trails for compliance. This phase ensures that modernization efforts remain adaptive, efficient and aligned with evolving business needs.

For clients, the agentic method for application migration and modernization provides a structured framework to engage agentic AI in the AMM context. The method helps reduce the number of work breakdown (WBS) activities, with agents managing and abstracting away a significant percentage of tasks. This optimization has a direct downward impact on time taken as well as costs in a modernization journey. 

At the same time, the method ensures that the human is still in the loop, especially for tasks that require the creation of thinking, novel solutions and activities such as authorization, credentials and approval.

Empowering agentic squads with autonomy can unlock speed and efficiency—but it must be balanced with thoughtful oversight. Transparency in how AI agents operate is essential to build trust and ensure alignment with program goals. Seamless integration and interoperability between human teams and agents is key to maintaining collaboration and continuity.

As roles evolve, skill development becomes critical—professionals must learn to manage, orchestrate and collaborate with AI agents. And with autonomy comes responsibility: robust security and governance frameworks are vital to mitigate risks, enforce ethical boundaries and ensure safe deployment of intelligent systems.

IBM is not just modernizing applications—it is redefining the entire process with agentic AI with the goal of delivering higher value and quicker ROI. With the agentic application migration and modernization method, legacy systems are transformed into intelligent, AI-native digital products. This method brings together reusable assets, gen AI-powered agents and agile squads to accelerate delivery while embedding governance, security and business value at every step.

To get started, reach out to your IBM representative and learn more about application modernization.