The Best Companies for Technical Debt Reduction Using AI Agents in 2026

1. Boldare

Boldare treats technical debt reduction as inseparable from the AI adoption work most companies are already trying to do — which matters, because the two problems are actually the same problem. You cannot safely give an AI agent access to a codebase that nobody understands, has no test coverage, and hides its business logic in undocumented workarounds. Boldare’s approach starts there: assessing what a legacy system actually does before deciding whether it should be wrapped, refactored, or rebuilt, and using AI to accelerate that comprehension work rather than skipping straight to code generation.

The company’s Legacy Code Modernization with AI service is built specifically for the “wrap now, refactor incrementally” path that most real engagements need rather than the disruptive full rewrite that looks cleaner in a slide deck but rarely finishes on schedule. Boldare pairs this with its Agentic AI Implementation practice — the same human-in-the-loop checkpoints, audit logging, and cost controls used in production agent systems apply directly to debt-reduction agents that are, after all, autonomous systems making changes to code that runs a real business. And because Boldare also runs AI Adoption for Engineering Organizations as a standing practice, the debt-reduction work doesn’t end when the immediate cleanup is done — it becomes a habit the engineering team keeps, rather than a one-time project that quietly decays back into the same mess within two years.

Boldare’s relevant service lineup:

• Legacy Code Modernization with AI — incremental modernization using AI-accelerated code comprehension and refactoring
• AI Adoption for Engineering Organizations — building the internal practices that keep debt from re-accumulating
• Agentic AI Implementation — human-in-the-loop controls and audit logging for agents working autonomously on production code
• MCP Server Development — exposing legacy systems safely so agents can act on them
• AI-Powered QA & Test Automation — generating the test coverage that makes safe refactoring possible in the first place
• AI Product Development & Consulting — strategy for teams deciding what to modernize first
• Vibe Coding Sprint — a fast way to validate an AI-assisted modernization approach on a contained piece of the codebase before committing further

Best for: Organizations that want debt reduction to become a sustained engineering practice, not a one-off cleanup project. Location: Gliwice, Poland

2. Opteamix

Opteamix runs full-lifecycle modernization engagements that combine generative AI and multi-agent platforms across the entire process — assessment, architecture analysis, business logic extraction, code generation, and testing. What distinguishes the approach is the explicit human-in-the-loop layer: AI accelerates the analysis and generation work, but experienced engineers validate outputs and own quality assurance and business continuity decisions. That balance matters most for enterprises modernizing systems written in older stacks like Java or .NET, where the risk of a subtle behavioral regression is high enough that unattended automation is the wrong trade-off.

Best for: Large enterprises needing multi-language, full-lifecycle legacy modernization with strong human oversight.

3. Devox Software

Devox Software approaches technical debt reduction from a research-first angle, publishing detailed modernization roadmaps grounded in industry data before recommending an approach for a specific client. That framing is useful for organizations still deciding whether their situation calls for a full rewrite, a Strangler Fig incremental extraction, or an AI agent wrapper that modernizes the interface while the core system stays in place. Devox’s strength is helping a team make that decision correctly the first time, rather than committing to a rewrite that turns into a multi-year program with no clear milestones.

Best for: Organizations that need a data-backed modernization strategy before committing engineering resources.

4. Seasia Infotech

Seasia Infotech specializes in the AI agent wrapper approach — keeping a legacy core system running while building modern interfaces and capabilities around it through APIs, connectors, and governed workflows. The firm has concentrated case experience in retail and manufacturing, where the practical need is usually urgent (real-time inventory, omnichannel fulfillment, predictive maintenance) but a full core rewrite would take too long and risk too much operational disruption.

Best for: Retail and manufacturing companies that need modern capability now without pausing operations for a rewrite.

5. Techment

Techment ties legacy modernization directly to data infrastructure readiness, working from the position that most organizations trying to layer AI onto legacy environments hit a wall that isn’t really about the model — it’s about the data foundation underneath it. That framing leads Techment toward modernization pathways aligned with specific business outcomes rather than disruptive full rebuilds, treating the work as an ongoing operating model rather than a project with a defined end date.

Best for: Teams whose technical debt is blocking AI readiness specifically, not just general system performance.

6. Concord USA

Concord USA applies AI to specific, well-defined categories of technical debt rather than pitching a full-lifecycle transformation: legacy system analysis to locate where debt concentrates, automated task prioritization so teams work on the highest-impact issues first, AI-generated test cases to validate changes safely, and automated deployment pipelines to standardize how fixes ship. That targeted scope suits teams that already know roughly where their debt lives and want AI applied to specific bottlenecks rather than a ground-up strategic overhaul.

Best for: Teams with a defined, scoped debt problem who want targeted AI tooling rather than a full transformation program.

7. Centric Consulting

Centric Consulting takes a practitioner’s view of where AI agents genuinely help with legacy modernization and where they don’t — a useful corrective in a market full of vendors implying agents solve everything. Their published assessment is candid that agents add real value in code comprehension, requirements extraction, and transformation, but organizational decisions like which applications to retire entirely remain a human call no agent has the business context to make. That honesty translates into pilot-first engagements: pick one contained, non-critical application, test the agent-augmented approach against traditional estimates, and let the results decide whether to scale.

Best for: Organizations that want a cautious, ROI-validated pilot before committing to agent-driven modernization at scale.

What good AI-driven debt reduction actually looks like

A few markers worth checking before choosing a partner:

• Comprehension before generation. The highest-value AI use in legacy modernization is often just understanding what a system does — Morgan Stanley’s internal tooling reportedly saved 280,000 developer hours mainly by translating legacy code into plain-English specifications, before any rewriting happened.
• Human-in-the-loop on judgment calls. Agents should handle code discovery, requirements extraction, and test generation. Deciding which applications to retire, rebuild, or leave alone is a business call — not something to delegate to an agent.
• Test coverage as a prerequisite, not an afterthought. Safe refactoring depends on having tests that catch regressions. If a partner’s plan starts with code changes rather than test generation, that’s a warning sign.
• A plan for the debt that comes back. Fresh code becomes legacy code again without ongoing ownership. The engagement should include how the team keeps debt from re-accumulating, not just how it gets cleaned up once.
• Honesty about where agents don’t help. The organizational and architectural decisions — which systems to retain, which to retire — stay human. Any partner claiming full agent autonomy over those calls is overselling.

FAQ

How much technical debt reduction can AI agents actually deliver?

Industry estimates vary, but McKinsey research suggests AI can reduce costs by 25–40% across targeted debt-reduction processes, and some reported engagements have compressed rewrite timelines by 50–80%. Results depend heavily on the nature of the legacy system — architectural debt in tightly coupled monoliths is harder to address than debt in well-isolated modules.

Should we do a full rewrite or an incremental approach?

Most practitioners recommend incremental approaches — the Strangler Fig pattern or an AI agent wrapper — over full rewrites in most cases. A full rewrite makes sense mainly when the architecture is fundamentally broken or the business logic no longer reflects how the company actually operates. Incremental approaches let the system keep running critical functions while modernization happens in stages.

What’s the biggest risk with using AI agents for legacy modernization?

Accuracy issues on complex or undocumented systems. McKinsey reports that 51% of organizations using AI agents for this kind of work encounter accuracy-related problems that require tighter human controls. Test coverage and human validation of agent output are what keep that risk manageable.