guardrails

Instead of asking an LLM “Did this work?” you define verification steps that produce a series of pass/fail artifacts. The agent can’t negotiate with a failing test. It either meets the specification or it doesn’t.

The 2026 GenAI Code Security Report found that roughly 44% of AI code generation tasks introduced a risky security vulnerability in tests. The average security pass rate across models is 56% – barely changed from 55% in the first report. In other words, security performance has stayed flat while the amount of AI-generated code entering pipelines has surged.
That is why GenAI code security is now a scale problem, not a theoretical risk discussion. If AI is responsible for half the codebase and vulnerable output remains this common, every organization needs a sharper strategy for model selection, verification, remediation, and governance.

The infrastructure that needs to exist to support this model at scale includes better automated regression detection, artifact-level validation that goes beyond diffing changed lines, reliable and fast environment provisioning, and guardrails that hold up under parallel workloads.

It's less about crafting individual assets and more about building systems with enough flexibility to serve a range of needs and enough structure to stay coherent at scale. Brand guidelines become agent-legible rules. A component library becomes the guardrails within which agents make decisions. An internal asset system that automatically tags images for color, text, and usage context means an agent can query for the right image rather than grabbing whatever's most recent.