Rather than programming AI agents with a company's formal policies, a more powerful approach is to let them observe thousands of actual 'decision traces.' This allows the AI to discover the organization's emergent, de facto rules—how work *actually* gets done—creating a more accurate and effective world model for automation.

AI coding's true enterprise value is limited because models struggle with legacy systems. Companies run on trillions of lines of mediocre code in old languages like COBOL—a problem that requires human intervention over decades, not a simple AI solution, which limits immediate, real-world impact.

The initial step in modernizing is not to rebuild, but to understand. AI can ingest source code, user manuals, and even screen recordings to map existing processes and identify optimization opportunities, ensuring the new system improves upon the old rather than just replicating it.

Vercel's CTO Malte Ubl notes that durable, resumable workflows are not a new invention for AI agents. Instead, they are a fundamental computer science concept that has been implemented ad-hoc in every transactional system, from banking in the 70s to modern tech giants, just without a standardized abstraction.

AI-driven approaches dramatically reduce the time and cost of modernizing legacy systems. What was once a multi-year, multi-million dollar mainframe project can now be completed in as little as 90 days, fundamentally altering the ROI for tackling technology debt.

When production code is the only source of truth, designers use AI to capture the live product and convert it back into a high-fidelity, componentized Figma file. This solves the common issue of undocumented engineering changes creating design drift.

Enterprises are trapped by decades of undocumented code. Rather than ripping and replacing, agentic AI can analyze and understand these complex systems. This enables redesign from the inside out and modernizes the core of the business, bridging the gap between business and IT.

Enterprises are finding immediate, high return on investment by using AI to port legacy codebases (like COBOL) to modern languages. This mundane task offers a 2x speed-up over traditional methods, unlocking significant infrastructure savings and even driving new developer hiring.

The transition to agent-centric workflows is not a simple software deployment; it's a complex re-engineering of business processes. This creates a huge opportunity for a new generation of consulting firms that specialize in getting organizations "agent-ready."