Using GitHub Copilot Agents for Legacy System Analysis: From Discovery to Delivery

Legacy modernization is one of the most consistently deferred initiatives in enterprise technology. Organizations know it’s needed, but the starting point feels too risky. GitHub Copilot Agents offer a practical entry point because they compress the discovery and analysis phase that stalls most projects before they begin.

In brief:

• GitHub Copilot Agents accelerate legacy modernization by rapidly consuming existing artifacts to generate structured feature parity requirements before a single line of new code is written.
• AI coding assistants are amplifiers: They accelerate what’s already working in your engineering process, but they also amplify what isn’t, making experienced human oversight nonnegotiable at every step.
• The right multiagent framework matters as much as the tools themselves, and context management, adaptability, and interoperability with your existing stack are what separate frameworks that deliver from those that stall.
• Your existing infrastructure — including coding standards, architecture decision records, and test frameworks — is directly usable as agent instruction material and should be treated as a modernization launchpad rather than background noise.
• The organizations that capture the most value from AI-assisted modernization are those that bring strong engineering discipline and senior practitioners who can direct agents and validate output at every phase.

Legacy systems don’t age gracefully. They accumulate undocumented business logic, circular database dependencies, forgotten integrations, and code that hasn’t been touched — or understood — in decades. And yet the applications nobody wants to work on are often the ones the entire business runs on.

A 2025 global research report found the legacy software modernization market is projected to reach $27.3 billion by 2029, underscoring the pressure organizations face to act. And according to a 2025 survey of U.S. information technology (IT) professionals, legacy modernization remains one of the most consistently deferred initiatives in enterprise technology.

Most organizations already know action is overdue. The challenge is finding a starting point that doesn’t introduce more risk than it resolves.

GitHub Copilot Agents have emerged as a practical entry point because they compress the phase most organizations get stuck in longest: understanding what they actually have. Legacy system modernization spans everything from infrastructure replatforming to application redesign, and GitHub Copilot — paired with the right artificial intelligence (AI) agent framework — offers a repeatable path into all of it.

How GitHub Copilot Agents Accelerate Legacy System Analysis

Legacy modernization follows two distinct phases:

• The first is reverse engineering, which includes determining what the existing system actually does and the business logic that was never documented.
• The second is forward engineering, or designing what the system should look like in a modern architecture and mapping a realistic path from one to the other.

GitHub Copilot accelerates both, but reverse engineering is where it delivers the most immediate value. When a developer points GitHub Copilot at a legacy codebase, the goal is to consume what exists: the source code, database structures, architecture diagrams, screen captures, even meeting transcripts and old requirement documents. Whatever is available becomes input.

The output is a structured set of requirements representing feature parity: a clear picture of what the system does today before any decisions are made about what it should do tomorrow.

One of our engagements illustrates the scale of what this makes possible. A client had three databases that had grown organically over 20 years, producing circular dependencies and redundant structures across 200,000 lines of SQL code. Untangling that manually to determine the correct order for rebuilding those databases would have consumed the better part of a week for a senior developer, possibly longer.

Guided by an experienced architect, AI agents resolved the dependency chain and generated the necessary scripts in hours.

That kind of compression matters most when complexity, not just volume, is the problem. Agents read code consistently. They surface what is there, reliably, as long as a skilled practitioner is validating the output at every step. That discipline is what makes the acceleration sustainable, and it is also what separates teams that get repeatable results from those that don’t.

“AI coding assistants are amplifiers. They amplify the good you have in your engineering process, and quite frankly, they amplify the bad parts too,” says Michael Collier, principal architect at Centric Consulting.

The results confirm it: When bswift partnered with Centric on mobile modernization, the engagement delivered 2,400 percent growth in app downloads and an industry-leading user rating.

That outcome did not happen by pointing a tool at a codebase and stepping back. It happened because experienced practitioners directed the process, reviewed every deliverable, and maintained clear quality standards throughout. AI-augmented development works at scale when the engineering discipline is already in place.

Complexity in legacy systems also extends well beyond the code itself. Infrastructure, deployment targets, downstream integrations, and the informal ways teams have learned to use a system over time? None of that lives in the source files.

At a certain scale of complexity, coordinating all of those moving parts across a full modernization workflow exceeds what any single coding assistant was designed to handle. That is where the conversation shifts from tools to architecture and where the choice of agent framework starts to determine whether a modernization project delivers or stalls.

Not All Agents Are Built the Same: 3 Factors That Separate Multiagent Frameworks

For well-scoped modernization efforts, GitHub Copilot Agents with strong human oversight may be sufficient.

But most enterprise legacy systems are poorly scoped. Enterprise legacy systems:

• Span multiple platforms
• Carry years of embedded business logic
• Connect to integrations the current team may not fully understand
• Require coordinating discovery, requirements, redesign, development, and testing as a connected workflow rather than a series of independent tasks

That coordination layer is what a multiagent framework provides. And the choice of framework matters more than most organizations realize before they are mid-project.

The frameworks available today — such as CrewAI, LangGraph, AutoGen, and purpose-built solutions like Agent C — each take a different architectural approach. What separates the ones that deliver from the ones that stall comes down to three factors:

• Context Management. Each agent in a multiagent system needs access to the right information at the right time. A framework that floods agents with irrelevant context or drops critical state between phases will produce inconsistent output regardless of how capable the underlying models are.
• Adaptability. Legacy codebases surface surprises constantly, including undocumented business rules, unexpected dependencies, and architectural decisions that made sense in a different era. A framework whose agents can adjust their behavior as conditions change, without requiring code rewrites, handles those surprises without derailing the project.
• Interoperability With Your Existing Stack. An agent framework that operates in isolation from your current tools adds friction at every handoff. One that connects natively to the platforms your team already uses can extend automation across the full development cycle.

Centric’s Insurance Analytics Platform work offers a useful illustration of that third point. Insurance carriers routinely deal with legacy architectures and mainframe dependencies that have calcified over decades. The ability to wire agent workflows directly into existing data pipelines and analytics infrastructure, instead of building parallel systems from scratch, is what makes that type of modernization feasible at enterprise scale.

Selecting the right framework for your modernization scenario is one of the most important decisions an organization can make before a single agent is built. Get it wrong, and you spend the project adapting your business process to fit the framework’s assumptions. Get it right, and the framework adapts to you and everything your team already has in place.

The most overlooked asset in most modernization projects is the infrastructure, standards, and institutional knowledge the organization has already built.

How to Use Your Existing Tech Stack as a Modernization Launchpad: 5 Steps

The instinct in most modernization projects is to treat the legacy system as the problem and everything surrounding it as neutral.

The reality is the modern infrastructure that organizations have already built around legacy systems — including their coding standards, architecture decision records, test frameworks, and reference implementations — is directly usable as agent instruction material. The launchpad is already there.

Here is the sequence that works in practice, drawn from legacy modernization engagements our team has led across industries.

Step 1: Start With Legacy System Analysis Using GitHub Copilot Agents

The goal of this phase is not to produce new code. It is to produce understanding. Feed the agents whatever exists — such as source code, database schemas, screenshots, documentation, transcripts, and more — and generate a structured set of feature parity requirements that reflects what the system actually does. Every deliverable at this stage requires human review and sign-off before moving forward.

Step 2: Feed Your Existing Standards in as Agent Instructions

In the same engagement where agents untangled 200,000 lines of SQL, the client provided their architecture decision records and coding standards up-front. Those documents became the foundation for the agents’ instruction set so that whatever was generated would align with how the team already wrote software, including naming conventions, method structure, test coverage expectations and all.

If those standards do not yet exist in a structured form, create them. They will pay dividends across every subsequent phase.

Step 3: Evaluate Agent Framework Fit Based on Complexity and Stack

Once you understand the scope of what you are modernizing, you can make an informed decision about whether GitHub Copilot alone is sufficient or whether a multiagent framework is warranted. The right questions to ask center on cost, complexity, and the realistic timeline for delivery, instead of which framework has the most features.

Step 4: Pilot on Feature Parity Work to Minimize Risk

The lowest-risk proving ground for AI-assisted modernization is work that does not require simultaneous functional and technical changes. Moving existing functionality to a new architecture without changing the underlying business logic lets you validate the process, build the team’s confidence, and produce a working reference implementation.

bswift’s mobile modernization followed this pattern: Establish the foundation, validate the approach, then expand.

Step 5: Expand the Process Across the Full Software Development Life Cycle

The patterns that work for legacy system modernization apply across the entire software development life cycle.

“It’s not just app modernization, it’s engineering modernization. It’s an opportunity to modernize your processes as well,” Collier says.

If a team is already working in GitHub or Azure DevOps, an agent can be wired into those systems to create stories, update work items, and validate pull requests against acceptance criteria. If designs live in Figma, an agent can pull the design system and verify that generated components match the right specifications.

The trends shaping legacy modernization in 2025 and beyond point consistently toward this kind of end-to-end integration: process-level change that compounds over time.

Legacy System Modernization: From Deferred to Delivered

Modernization is often deferred for legitimate reasons. Legacy systems are complex, the documentation is incomplete, and the risk of disrupting something the business depends on is real. None of that disappears with AI.

What changes is the rate at which experienced practitioners can move through the work. Discovery that once took months can take weeks. Dependency analysis that would have required days of manual review takes hours. The entry point that used to mean months of groundwork before anything useful could be delivered now starts with whatever artifacts already exist.

The organizations best positioned to capture those gains are the ones that bring strong engineering discipline to the work. They bring clear standards, meaningful human oversight, and senior practitioners who know what good output looks like and can direct agents accordingly. For those organizations, GitHub Copilot Agents are a practical starting point available right now.

If your organization is ready to explore what AI-assisted modernization could look like in practice, learn more about Centric’s AI Augmented Development services or connect with our team to start the conversation.