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1-Minute Key Takeaways
- Successful mainframe and midrange modernization starts with understanding application functionality, business rules, data relationships and dependencies before making changes.
- Trusted Application Intelligence, derived through deterministic source code analysis, provides the accuracy, traceability and application context needed to make informed modernization decisions.
- Deep application knowledge graphs and AveriSource Platform capabilities such as MicroTracing™ help teams understand how individual components connect to broader business functions and trace findings back to their source.
- AI coding assistants and autonomous engineering agents can accelerate modernization, but they need comprehensive, trusted application intelligence rather than relying on AI models alone to interpret complex legacy codebases.
- The right modernization strategy varies by application and business objective, whether refactoring, replatforming, reimagining, replacing or retaining existing functionality.
- Combining trusted application intelligence with hybrid modernization expertise enables organizations to reduce risk, make better-informed decisions and confidently transform applications across mainframe, cloud and distributed environments.
Before modernizing a legacy mainframe or midrange application, teams need to understand what it does, how it interacts with other systems, where its business rules reside and which behaviors must be preserved. That understanding has always been fundamental to successful modernization. In the AI era, it becomes even more important.
AI-assisted engineering can accelerate modernization, but only when the tools and agents involved have access to accurate, comprehensive and verifiable knowledge of theapplications they are changing.
That is where AveriSource’s approach begins.
The Challenge Behind the Code
Modernization involves decisions about business behavior as well as technology. A calculation, validation rule or batch process may serve a purpose that is not clear from an individual program. Changing it requires understanding how it fits into the wider application.
When documentation is incomplete, teams need to build that understanding through code analysis and input from people who know the system. This work helps answer practical questions.
- What is the scope of the change?
- Which dependencies need attention?
- What business rules must be preserved?
- How will the team verify the result?
These questions also matter when introducing AI-assisted engineering. Engineers need to understand the information behind a recommendation and check whether it accounts for the application’s requirements.
What is Trusted Application Intelligence?
AveriSource uses the term Trusted Application Intelligence to describe structured interconnected application knowledge derived through deterministic analysis of source code.
The AveriSource Platform goes beyond basic code inventory and documentation. It analyzes application logic, business rules, data structures and dependencies, building a deep application knowledge graph that connects individual components to the broader business functionality they support.
Through Plaform capabilities such as MicroTracing™, teams can examine application behavior at the statement and variable level, trace business logic and data access to its source, and isolate the business functions and dependencies that need to be understood before making changes.
Application intelligence is organized into actionable deliverables, including business function specifications, application models and dependency information. Teams can investigate findings, understand their context and trace them back to the underlying source code.
The result is an evidence-based understanding of the application estate that provides a trusted foundation for modernization decisions and AI-assisted engineering.
How does Application Intelligence support AI-assisted Modernization?
AI coding assistants and autonomous engineering agents are creating new opportunities to accelerate mainframe modernization. However, their effectiveness depends on the quality and completeness of the application context available to them. Used alone, AI shouldn’t serve as an authoritative system of record for complex legacy applications as it can’t provide the accuracy, traceability, or comprehensive application understanding required for enterprise legacy modernization.
AveriSource bridges this gap by making trusted application intelligence accessible to AI agents and engineering tools through MCP and APIs. Rather than relying on AI models alone to interpret complex legacy codebases, teams can provide these tools with structured, source-traceable knowledge of application functionality, business rules, data relationships and dependencies.
This creates a foundation for AI-forward engineering workflows, including business function analysis, requirements development, solution design, code generation and testing.
AveriSource's approach combines proven deterministic application analysis with the flexibility of generative and agentic AI. The Platform establishes the trusted application knowledge; AI tools and engineering teams use that knowledge to accelerate the work of transforming applications.
Human review, business validation and appropriate testing remain essential throughout the process.
How do you Choose the Right Modernization Path?
The right approach depends on the application’s role, technical requirements and the business reason for changing it.
Different applications require different modernization strategies. Depending on business objectives, technical constraints and application complexity, the right approach may be to refactor or replatform existing functionality, reimagine applications to support changing business requirements, replace functionality with commercial solutions, or retain applications where there is no compelling reason to change.
An assessment helps teams evaluate these options. It establishes what an application does, what it connects to and which constraints need to shape the plan. This understanding can inform workload grouping and migration sequencing, including how applications that move will interact with those that remain on the legacy platform.
AveriSource’s hybrid modernization approach supports a combination of paths across mainframe, cloud and distributed environments. Decisions start with the client’s priorities and the evidence gathered from the application estate.
From Understanding to Implementation
Application intelligence needs to also support the people responsible for delivering the change. AveriSource combines the AveriSource Platform with Hybrid Modernization Services, covering assessment, roadmap development, implementation, testing and ongoing optimization.
The Platform provides the trusted application intelligence. Modernization specialists help teams use the intelligence to define the work, choose an approach and carry it through delivery.
Conclusion
Modernization Built on Intelligence reflects this connection between understanding applications and changing them. Start with a business priority. Identify the applications that support it, establish how they work and understand the dependencies and requirements that will shape modernization. With trusted application intelligence as the foundation, organizations can make better-informed decisions and move forward with confidence.
Talk to us about your mainframe and midrange applications and the business priorities shaping your modernization plans.