AI-Driven Software Delivery: Quality, Security & Operations
AI increases the speed and volume of software change. The new challenge is to be able to generate more code while knowing which changes are safe to release, reliable to run, and worthy of trust. After attending, engineers and architects will be able to design, supervise, and secure agentic workflows across the software development lifecycle with the right context, permissions, sandboxes, checkpoints, cost controls, and human oversight.
From AI-Accelerated Change to Production Confidence
Learn from Industry Leaders about:
- Define risk-based testing, evaluation, and acceptance criteria for AI-assisted changes
- Combine automated quality gates with focused human review and engineering judgment
- Apply threat modeling, secure coding, vulnerability detection, and software supply-chain controls
- Manage dependencies, secrets, licenses, software provenance, and non-human identities
- Build policy-aware CI/CD pipelines with auditable approvals, compliance evidence, and clear release criteria
- Observe agent actions, code changes, delivery pipelines, inference behavior, and production outcomes
- Build reliable AI-native capabilities with RAG, AI gateways, structured outputs, guardrails, model routing, and enterprise integrations
- Balance cost, latency, quality, and business value through observability, attribution, quotas, and FinOps practices
- Verify production readiness through runnable checks, audit trails, rollback, incident containment, credential revocation, and recovery strategies