The Prompt is the Product: Field Lessons in AI-Powered Modernization
With AI coding agents evolving from simple autocomplete to autonomous reasoning engines, enterprise teams face a critical question: Can these tools actually modernize legacy systems at scale? Our field benchmarks - including a 9.5-million-token enterprise monolith - reveal the answer isn't about which tool you choose, but how you wield it.
The era of autonomous AI coding agents is here - but are they ready for enterprise modernization?
We put today's most advanced CLI-based agents - Claude Code, OpenAI Codex, and our structured G.Tx workflow - through rigorous field tests to determine what actually works when modernizing legacy code at enterprise scale.
From batch-processing independent COBOL scripts to navigating the complexity of a 9.5-million-token monolith, our findings reveal a critical truth: while models provide the raw horsepower, success is defined by the strategy you use to wield them.
Who is this report for?
For CTOs & Heads of IT: Learn why building capability around "AI-augmented engineering" matters more than purchasing the perfect agent. Discover governance frameworks that actually work.
For System & Solution Architects: Understand how to treat LLMs as components in a modernization architecture - not black boxes operating autonomously. Get practical guidance on workflow design, testing strategies, and human-in-the-loop integration.
Wondering if this benchmark report is the right fit for you?
Understand the evolution of AI coding tools
From autocomplete to context-aware IDEs to fully autonomous agents - discover the three phases of AI coding evolution and where we stand today.
Get real benchmark data, not marketing claims
See actual field test results from 9 benchmark runs across three tools, including pass rates, code coverage, duration, and variance analysis.
Understand the "Green Build Illusion"
Discover why a 100% test pass rate doesn't guarantee functional equivalence—and why AI "grading its own homework" is a recipe for hidden failures.
Compare autonomous agents vs. structured workflows
Find out when "black box" agents deliver value (edge cases, exploration) and when structured workflows achieve 3–4x speed advantage.
See how Context Engineering determines success
Our control experiment proves that the quality of the prompt - not the model - is the decisive factor between 99% accuracy and broken business logic.
Discover the hybrid model for enterprise
Get a practical framework: workflows for high-volume pattern processing, agents for complex exceptions - with human oversight at critical gates.
Scale your legacy modernization 5x faster without business disruption
Legacy transformation services powered by Agentic AI-driven G.Tx Platform