OutSystems has unveiled the general availability of Agent Experience, a move that reflects a growing enterprise challenge: how to harness the speed of AI coding agents without compromising governance, security or software quality.
The new offering allows organizations to use popular AI coding tools such as Claude Code, Cursor, Codex and Kiro while maintaining centralized architectural control and compliance requirements. The announcement comes at a time when enterprises are increasingly embracing AI-assisted software development but remain concerned about production readiness and regulatory risk.
A key driver behind the strategy is the growing realization that AI-generated code alone is not enough. According to OutSystems, recent developer survey findings show that 74% of AI-generated code requires significant rework or never reaches production, highlighting the gap between experimentation and enterprise deployment.
“Coding agents have unlocked unprecedented velocity, but the real friction is operational overhead and fragmented code. Agent Experience delivers the speed of AI coding tools with the compliance, security and scalability required for mission-critical systems.”
– Woodson Martin, CEO, OutSystems
Rather than allowing coding agents to generate fragmented applications independently, OutSystems positions Agent Experience as a governance layer where AI agents define system architecture while the platform generates code deterministically under predefined controls. This approach aims to preserve architectural consistency, automate testing and enforce security policies from the outset.
Early adopters are already reporting tangible results. Sign manufacturer YESCO has reduced software release cycles to two weeks, while US law firm Lowenstein Sandler built a production-ready legal application in under three hours. Danish retailer Normal reportedly reduced development timelines from six weeks to two.
The launch signals a broader shift in enterprise AI adoption, from using coding agents as productivity tools to embedding them within governed software development lifecycles. As AI regulation tightens and enterprises move more applications into production, platforms that balance developer freedom with enterprise-grade oversight could become critical to the next phase of AI-driven software engineering.