Agentic coding shifts the engineering bottleneck from implementation to verification. As agents generate more code, teams struggle to ensure that output aligns with product intent, design constraints, and cross-team contracts, especially at scale.

This session explores a spec-driven verification architecture for AI-native development. Instead of treating specifications as static documentation, we turn them into structured, machine-readable context that agents can consume, and be verified against.

We’ll go deep into three technical layers:

1. Spec Ingestion from Ticketing Systems
How to retrieve structured requirements from systems like Jira or Linear, normalize them into machine-readable artifacts, and bind them to pull requests and agent workflows. We’ll discuss parsing strategies, schema design, and avoiding ambiguity in loosely written tickets.

2. Design Verification via Figma MCP
How to inspect design constraints programmatically using Figma through MCP-based integrations. We’ll cover extracting spacing, typography, color tokens, and layout rules, and validating UI implementations against design intent, beyond visual regression testing.

3. Secure Execution with Isolated Sandboxes (AWS Agent Core as an example)
How to spin up ephemeral, permission-bounded sandboxes for runtime verification of agent output. We’ll examine isolation strategies, environment scoping, observability hooks, and how to safely execute verification logic without expanding blast radius.

Along the way, we’ll share failure modes and scaling lessons from real-world implementations (with a lot of self humor and users' reactions :) including what breaks when specs are vague, when design tokens drift, and when verification isn’t treated as a first-class concern.

This session brings a fresh, field-tested perspective from building and operating a spec-driven verification architecture in real engineering environments