Code Generation & AI-Assisted Development

AI pair programmers that actually ship.

6 sessions at the summit3 external resources

Overview

AI-assisted coding has moved from autocomplete to full agentic workflows. Amazon Q Developer and Kiro provide inline suggestions, transformations (Java upgrades, .NET porting), security scans, and full agentic edits across files. The shift from "vibe-coding" to spec-driven development addresses quality, traceability, and trust. Effective use combines AI with strong testing, code review, and observability.

Key concepts

  1. Inline suggestions vs. agentic edits vs. autonomous swarms
  2. Spec-driven development with Kiro
  3. Code transformation: Java upgrades, .NET porting, mainframe
  4. Security scanning and SBOM with Q Developer
  5. Productivity measurement (DORA metrics for AI)

Key AWS services

  • Amazon Q Developer
  • Kiro
  • AWS CodeWhisperer (legacy)

Learn more — curated resources

Hand-picked official docs, foundational papers, and the best community guides for going deeper on this topic.

Sessions on this topic

6 sessions from the Summit covered this topic. Each is a self-contained mini-lesson.

  1. DVT201Intermediate

    Building Software like never before with agentic AI

    Discover the future of software development as we explore the transformative power of agents with Kiros spec-driven development in modernizing your entire software development lifecycle (SDLC). Agentic AI is impacting software development by automating tasks, improving efficiency, and enabling more autonomous workflows throughout the development lifecycle. It allows teams to go beyond simple code generation to handle project planning, designing, testing, documentation, building agents into workflows, and retiring technical debt. Join us to explore how these powerful capabilities work together to help organizations accelerate from prototype to production ready applications.

  2. DEV209Intermediate

    CI/CD Guardrails for Agentic Coding Workflows

    AI coding agents introduce failure modes traditional CI/CD pipelines weren't built to catch — deleted tests, weakened type constraints, silent cross-service regressions. This session examines practical pipeline-level guardrails for agentic workflows running on ECS Fargate and distributed CI environments. You'll learn which failure patterns agents introduce that humans rarely do, which automated checks reliably catch them, and how to structure pipelines that apply appropriate scrutiny to agent-generated code without blocking developer velocity. Leave with concrete, implementable patterns covering test integrity enforcement, type safety validation, and cross-service regression detection — applicable whether you're managing one agent or coordinating many across multiple repositories.

  3. ISV201Intermediate

    MCP on EKS: Xero's AI-Driven Developer Experience

    AI coding agents are transforming how developers build and operate modern cloud-native applications. With tools such as Kiro CLI, Kiro IDE, or any MCP-compatible AI coding assistant, developers are embracing AI to move faster and scale smarter. This session explores how MCP servers help developers streamline code generation, deployment, and debugging by embedding infrastructure awareness directly into the AI assistant. Learn how Xero is leveraging MCP to speed up development, simplify operations, and deliver more reliable containerized apps at scale. Xero will also share their success story using Kiro CLI, Prometheus MCP, EKS MCP, and AWS Knowledge Base MCP to identify and resolve Prometheus cost spikesslashing costs by 40%.

  4. TNC203Intermediate

    Structured Approach to AI coding with Spec-Driven Development on Kiro

    This session demonstrates how Kiro brings discipline and clarity to AI-assisted software development, ensuring generated code aligns with intended functionality and architecture. Explore Kiro's innovative spec-driven development approach for AI coding. Learn how to leverage structured specifications as a single source of truth, contrasting with unstructured 'vibe coding'. Discover how Kiro uses AI to generate detailed requirements, design, and task documents, guiding AI agents in code creation. Experience a workflow that enhances collaboration, maintainability, and documentation accuracy.

  5. DEV306Advanced

    Taming Legacy Code: Multi-Agent AI in Brownfield Systems

    Real engineering happens in legacy codebases, not blank canvases. This session explores deploying multi-agent AI workflows using Kiro against brownfield production systems with tangled dependencies and accumulated technical debt. Learn how to orchestrate specialised agents for system mapping, dependency navigation, code generation, and validation within complex existing architectures. We'll examine practical strategies for providing sufficient context to agents, implementing guardrails to prevent regressions, and coordinating multiple agents toward shared goals. Walk away with actionable techniques for applying agentic AI to real-world codebases, understanding where automation delivers value and where human judgment remains irreplaceable.

  6. STP216Intermediate

    Building AI Agents: From Open-Source Frameworks to Production-Grade

    AI agents are moving from demo to deployment. Startups across ANZ are building production-grade assistants using open-source orchestration frameworks, fine-tuned foundation models, and GPU-accelerated inference on AWS and NVIDIA infrastructure. This panel explores what it actually takes to ship agentic use casesfrom choosing the right models and frameworks to managing latency, cost, and reliability at scale. We'll hear from AirTree VC on where the investment thesis is heading, from NVIDIA on how accelerated compute is shaping the agent stack, and from Heidi Health building and scaling these systems in production. Whether it's vertical agents for healthcare, customer support, or code generation, we'll focus on what's working, what's hype, and where the real startup opportunities lie in the agent ecosystem.

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Non-obvious insights

From the Playbook

One sharp, contrarian insight per session — the things teams don't think of unprompted.

The team skill that matters most in spec-driven development is *product clarity*, not coding skill. Your PMs and tech leads become the bottleneck. Plan for that — invest in PM training on spec writing now, before the AI tooling forces the issue. ---DVT201 — Building Software like never before with agentic AI
The 40% Prometheus cost cut came from agents finding *cardinality bombs* humans missed. The gain wasn't AI being smarter — it was AI being patient enough to read every metric label. Use AI for tedious-but-tractable problems; that's where the 80% of the wins live. ---ISV201 — MCP on EKS: Xero's AI-Driven Developer Experience
Spec-driven development surfaces spec quality immediately — bad specs produce visibly bad code. That's a feature. Use it as a forcing function for product clarity rather than an inconvenience. ---TNC203 — Structured Approach to AI coding with Spec-Driven De…
The right number of agents is rarely the obvious one. Two specialists usually outperform five. Multi-agent designs that look organised in diagrams often thrash in production. Default to fewer until you can name the specific reason you need more. ---DEV306 — Taming Legacy Code: Multi-Agent AI in Brownfield Sys…
VC investment thesis is shifting from "agent capability" to "agent vertical depth." Generic agents are commoditising fast; domain-specific agents have moats. If you're raising for a generic agent platform in 2026, you're raising in a saturated market. ---STP216 — Building AI Agents: From Open-Source Frameworks to P…