Overview
AWS for Industrial connects OT (operational technology) and IT to drive Industry 4.0. AWS IoT SiteWise collects, structures, and analyzes industrial equipment data at scale. AWS IoT TwinMaker builds digital twins. Amazon Lookout for Equipment / Vision applies ML for predictive maintenance and quality. Generative AI is now used for anomaly explanations, root-cause analysis, and operator assistance.
Key concepts
- OT/IT convergence and the Purdue model
- Asset hierarchies and industrial data models
- Predictive maintenance and condition monitoring
- Digital twins and simulation
- Edge processing for latency and resilience
Key AWS services
- AWS IoT SiteWise
- AWS IoT TwinMaker
- Amazon Lookout for Equipment
- Amazon Lookout for Vision
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
19 sessions from the Summit covered this topic. Each is a self-contained mini-lesson.
- ISV302Advanced
Architecting Scalable AI Agents using Amazon Bedrock AgentCore
Discover how to build powerful AI agents using Amazon Bedrock's suite of tools, with a focus on Amazon Bedrock AgentCore. This session explores how Parrot Analytics leveraged the modular components of Amazon Bedrock AgentCore and Amazon Nova foundational models to achieve 10x the processing speed of manual classification across 2M+ entities. We will dive into prompt and context engineering, knowledge bases, and observability for production agentic workloads.
- DEV207Intermediate
Data Observability Without the Pain - Lessons from a Production System
Modern IoT platforms are inherently data platforms. Events flow through APIs, queues, AWS Lambda Serverless functions, storage systems, and device networks before becoming meaningful data. When something goes wrong, tracing a single event across these distributed components quickly becomes painfuland the question shifts from _what happened_ to _where do I even start looking Ill walk through three practical observability patterns drawn from building and operating a production, event-driven IoT healthcare platform on AWS that processes tens of thousands of device events daily. Using OpenTelemetry, AWS X-Ray and Honeycomb, well explore techniques for gaining visibility into asynchronous event pipelines, correlating activity across services, and tracing events as they move through distributed systems. Youll leave with three concrete patterns you can apply immediately to your own event-driven data systems.
- STP215Intermediate
How Sonder Improve 24/7 Employee Wellbeing with AWS AI
Sonder's mission is to empower people to be at their best, delivering the right care at the right time to over 1 million members globally across employers, universities, and insurance partners. To scale this mission without compromising quality, Sonder built an AI Copilot entirely on AWS, a 100% human-in-the-loop system designed with safety-first approach where Care Specialists remain at the centre of every interaction, with reinforcement learning from human feedback (RLHF) continuously improving the Copilot's performance. This session explores the architecture behind it, including real-time triage, knowledge retrieval, and escalation workflows and shares the tangible business impact: faster response times, greater operational efficiency, and new service and revenue opportunities unlocked by AI.
- STP210Intermediate
TeamForm's Generative Dashboards with Strands & Bedrock AgentCore
Most teams are still piloting AI - TeamForm is shipping it. In this session, we show how we built enterprise and production-ready generative dashboards in weeks on AWS Bedrock and AgentCore, and how an AI-native operating model made that velocity possible. Learn what it actually takes to operationalise AI across product and engineering, not just prototype it.
- ARC304Advanced
Demystifying Agent Identity
Confused by inbound vs. outbound authentication for agents You're not alone. This Level 300 session demystifies OAuth flows and agent identity patterns through the lens of a practitioner's learning journey. Explore the differences between SPA (single-page web app) and agent authentication, then dive into AgentCore's inbound/outbound auth with Runtime and Gateway. Through live code demonstrations of 3-legged OAuth flows, you'll see exactly how agents authorize actions on behalf of users. Leave with working code examples from aws-samples and practical implementation insights to accelerate your agent development. Part of the AgentCore session track.
- STP208Intermediate
NextAI's LegalScout: A Data Foundation for Private Legal AI
LegalScout helps Australian SME law firms turn Generative AI into a competitive advantage by securely leveraging their own client data and confidential matters to work smarter, not harder. Built with Australian lawyers on AWS using Amazon Bedrock for inference and Amazon S3Vectors for secure document searches, it automates repetitive work, streamlines workflows, and improves drafting, contract review, and research to boost productivity, reduce costs, and lift accuracy while maintaining strict privacy and compliance.
- ISV304Advanced
Managing AI Agents with Confidence and Control using Kasada & AWS
AI agents are powerful but riskythey can access sensitive data, trigger workflows, and make autonomous decisions. Kasada and AWS are helping enterprises adopt agents with confidence through comprehensive AI agent trust management that protects legitimate AI agents while detecting and blocking malicious automated visits. Kasada's platform, integrated with AWS services, enables organizations to distinguish trusted agent traffic from sophisticated bot threats, monitor for anomalous behavior, and maintain agent integrity against evolving AI-powered attacks. Join Kasada and AWS experts to explore a practical framework for managing agent trust: how AI and agentic traffic are being abused today, where risks appear across discovery and checkout, how teams decide when to allow or block agents, what new protocols like Web Bot Auth do and where they fall short, and what Kasada has built for agent traffic visibilityall while maintaining seamless customer experience.
- SEC302Advanced
Leap ahead in Cloud Operations with AWS DevOps Agent
Downtime costs revenue. Alert fatigue burns out your best engineers. Manual incident investigation wastes hours that could be spent building. Every cloud team faces these operational challenges, yet most still rely on tribal knowledge and context-switching across multiple tools to diagnose issues. In this session, we demonstrate how AWS DevOps Agent transforms incident response from hours of manual investigation to minutes of autonomous analysis. Watch as the agent automatically correlates data across your observability tools, identifies root causes, and delivers actionable mitigation plans freeing your team to build instead of firefight.
- DEV210Intermediate
AI-Driven Incident Triage: From Slack Alert to Root Cause
Modern AWS environments generate more alerts than teams can realistically investigate. This session demonstrates a proof-of-concept that transforms Slack alerts into automated investigation workflows using AI.Learn how to trigger parallel queries across CloudWatch, Amazon EKS, Prometheus, and deployment history when an alert fires — returning correlated summaries with probable causes and dashboard links directly in Slack.You'll leave understanding practical integration patterns for AI-assisted triage, telemetry hygiene requirements, and guardrails for safely introducing AI into production incident response. Discover how AI augments — rather than replaces — your existing observability stack, meaningfully reducing time-to-insight during incidents.
- ARC307Advanced
AI Powered Resilience Lifecycle
Not all disaster recovery strategies can address the complex, dynamic nature of modern cloud infrastructures, leading to gaps in system resilience and compliance adherence. Discover how to enhance resilience and disaster recovery on AWS empowered by AI. This approach bridges infrastructure insights and application-level testing, enabling more effective disaster recovery preparation. You will learn how to leverage Large Language Models (LLMs) with AWS Resilience Hub and AWS Systems Manager to modernize testing, analyze infrastructure, and generate targeted AWS Fault Injection Service experiments and recovery runbooks. Walk away with practical examples of automated test generation with templates and learn to design prompts.
- WPS203Intermediate
Optimising Outpatient Waitlists with ML at Gold Coast Health
Deploying ML in high-stakes environments demands enterprise readiness, governance, and continuous monitoring. In this session, you'll learn how Gold Coast Health moved from pilot to production with a predictive model identifying patients unlikely to attend procedures — achieving 33% precision, doubling the 15% manual baseline — while ensuring fairness across cohorts. The session covers real-world ML architecture on Amazon SageMaker Pipelines, production monitoring including data quality, pipeline health, and drift detection, plus navigating AI governance through bias analysis and impact assessment. Whether you're in healthcare, financial services, or any regulated industry, walk away with actionable patterns for deploying responsible ML at scale.
- 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.
- ISV211Intermediate
Scaling Conversation Intelligence with Agentic AI on AWS
Businesses capture millions of conversations daily, sales calls, support interactions and compliance discussions, yet most of this intelligence remains locked away. Standard dashboards and predefined reports cannot address every customer's unique questions. Dubber, a world leader in conversation capture and intelligence, built Insight Agent on AWS, enabling users to ask bespoke, natural language questions across conversations and structured data to receive context-aware answers in seconds. Learn how Dubber innovated from static dashboards to surfacing business value moments, and now to agentic AI that compresses time to value, making conversation intelligence accessible, scalable and viable.
- IND204Intermediate
How Transurban Transformed Customer Experience with AI Agents on AWS
Every month, Transurban handles 5.5 million customer interactions across its Linkt brand — and is reimagining every one of them. Built on Amazon Connect, Transurban's AI-powered customer service platform has evolved from simple chatbots to multi-turn conversational AI and personalised experiences that are already lifting bot containment and freeing agents for higher-value work. Join this session to hear how Transurban is aligning people, process, and AI to transform customer service, what's coming next with Amazon Connect Cases and Email, and the hard-won lessons from scaling AI in a complex enterprise.
- STP301Advanced
AI-Native Remediation with Pleri: Your Security Engineer That Ships
Most security tools find the problem and hand it to a human. Plerion closes the loop. In this talk, we'll show how Pleri, our AI security engineer powered by Amazon Bedrock, takes a critical cloud risk from detection to remediation without the alert-ticket-backlog cycle. Watch a top risk get prioritized, a ticket filed, a PR opened, and code-level remediation land in your environment. Re-define what it means to have an AI teammate that does the work, not just alerts and reporting.
- IDE101Foundational
From principles to practice: Scaling AI responsibly
Building AI applications that customers trust requires more than technical excellenceit demands a deliberate approach to managing risk across every stage of the AI lifecycle. As organizations scale their AI initiatives, the challenge of balancing innovation speed with responsible AI practices across dimensions like privacy, security, fairness, safety, and explainability becomes increasingly critical. Join our panelists for a 30-minute discussion where they will explore: Practical approaches to embedding responsible AI principles into AI application development without slowing down innovation, key considerations across privacy, security, fairness, safety, and explainability that organizations should prioritize, lessons learned from building AI applications that earn and maintain customer trust, and strategies for navigating the evolving responsible AI landscape and managing risk at scale. Whether you are a technical leader building AI solutions, a business decision-maker shaping your organization's AI strategy, or a practitioner looking to deepen your understanding of responsible AI, this session will provide actionable insights to help you build AI applications that are not only innovative but also trustworthy.
- STP101Foundational
Driving Profitable Growth with Generative AI: From Prompt to Product
Generative AI is the largest disruption to software company business models since the emergence of SaaS. In this workshop we'll cover best practices software companies use to take AI-native products from pilot to production, including identifying use cases that drive business value, features that accelerate adoption and pricing strategies that result in profitable growth. This will include an interactive session - so bring your ideas and collaborate with other startups to help find generative AI features that add value to your product
- DEV206Intermediate
AI Isnt Just for Developers: Using Kiro CLI & AWS MCP for Cloud Ops
You cant turn your head sideways without seeing a slew of articles, blogs, or videos about AI, and most of them focus on developer tooling and writing code. But AI isnt just for developers. Its an incredibly powerful tool for operations folks, too.In this lightning talk, Ill share how I use Kiro CLI and the Kiro console with AWS Model Context Protocol (MCP) integrations for day-to-day cloud operations. From information gathering and log analysis to reporting and IAM policy interpretation, these tools help reduce cognitive load and speed up your output when working with AWS environments.Ill also discuss how I used Kiros spec-driven development approach to build a Python-based reporting tool, despite not being a software developer.This session is designed to make AI tooling feel approachable and practical for anyone working in AWS — not just developers.
- ISV214Intermediate
Grounding AI Agents: How to give your AI real-world data with MCP
Most AI agents fail not because of models, but because they cant access trusted external data. This session shows how InfoTrack used Model Context Protocol (MCP) to connect agents to authoritative data sources via a compliant and secured gateway.
Non-obvious insights
From the PlaybookOne sharp, contrarian insight per session — the things teams don't think of unprompted.