Most companies are adding AI agents to legacy systems rather than modernizing how the work actually gets done. That may create isolated productivity gains, but it rarely removes the outdated applications, fragmented data, manual handoffs, and hidden dependencies that continue to slow the business.

Key Takeaways:

• Why adding agents on top of legacy systems often preserves the bottlenecks enterprises are trying to eliminate.

• How to identify the applications, data, workflows, and dependencies that must be modernized before agentic use cases can scale.

• How AI agents can accelerate discovery, mapping, validation, test generation, and repetitive modernization work.

• Where experienced engineers must retain ownership of architecture, judgment, risk, and cutover.

• How to progress from assessment to a sequenced modernization plan and then into production execution.

• How to reduce reliance on internal engineering capacity while moving modernization and AI initiatives into production.