From April 22 to April 24, 2026, the Mandalay Bay Convention Center hosts Google Cloud Next, an event that has moved past the experimental phase of artificial intelligence into the grit of production-grade systems. This year, the focus is squarely on agentic AI, infrastructure that actually scales for large models, and the reality of securing these environments against sophisticated threats.
Wednesday kicks off with an opening keynote at 9:00 AM, setting the stage for three days of intense learning, networking, and product reveals. Thursday offers the Developer Keynote and the evening social event, Next at Night, while Friday concludes with a final slate of sessions focused on long-term execution. Organizations are attending this event to determine how to operationalize AI without blowing their budgets or compromising their data.
The main narrative this year centers on the transition from simple chat interfaces to autonomous agents. These systems now perform multi-step reasoning and execute tasks across enterprise platforms. Attending this conference provides a direct look at how to build, govern, and protect these agents. Use this Google Cloud Next 2026 guide by role to identify the sessions and tracks that will actually deliver value for your specific role.
CTOs and Technology Strategy Leaders
Executive leadership needs a clear line between technical potential and fiscal reality.
- Opening Keynote: Attend for the high-level roadmap and major partnership announcements that dictate market direction.
- The AI Balancing Act: Regulation vs. Innovation: This session provides a framework for scaling new tech while meeting global compliance standards.
- Enterprise AI, Realized: Scaling Trust, Speed, and Value: Focuses on the strategic shift from pilot projects to full-scale production.
- Understanding AI ROI in the Modern Enterprise: A data-driven look at how to measure the financial impact of cloud-native AI.
- Leaders Circle: The Future of Agentic Commerce: Networking and insights on how autonomous systems are changing consumer interactions.
Appropriate Track: Technology & Leadership. This track provides the high-level perspective necessary for making informed purchasing and hiring decisions.
Cloud Architects
Architects need to design systems that are both resilient and flexible enough to handle the compute demands of modern models.
- Architecture for AI Workloads: Learn the specific compute and storage configurations required for low-latency inference.
- Building a Multicloud Open Data Lakehouse: Essential for organizations avoiding vendor lock-in while maintaining a high-performance data layer.
- Designing for Zero-Trust in an AI-Driven Environment: Technical blueprints for securing decentralized cloud resources.
- Scaling Infrastructure with Gemini and Vertex AI: How to use Google’s core tools to automate environment provisioning.
- Migration Strategies for Legacy Data to AI-Ready Platforms: Practical steps for preparing old datasets for new intelligent systems.
Appropriate Track: Architecture. This track focuses on the foundational structures that support every other cloud function.
Data Engineers
The quality of an agent is tied to the quality of its data. Engineers must focus on the pipelines that feed these systems.
- Supercharge Data Science with Gemini and BigQuery: Deep dive into using generative assistants to write and optimize complex SQL.
- Zero-Copy Sharing with SAP, Salesforce, and ServiceNow: Learn how to integrate massive datasets without the overhead of traditional ETL.
- From Chaos to Command: Building Your Unified AI Data Hub: A look at centralizing data governance in a fragmented environment.
- Automated Data Lineage in the Agentic Era: How to track data provenance when autonomous agents are the primary consumers.
- Real-Time Decisioning with Intelligent Data Pipelines: Technical session on reducing latency in data delivery.
Appropriate Track: Data Analytics. This track covers the entire lifecycle of data from ingestion to actionable insight.
Machine Learning Engineers
The focus for ML engineers has shifted to fine-tuning and deploying agents that can actually reason.
- Deploying Models Using Vertex AI: A practical guide to the latest deployment workflows and monitoring tools.
- Orchestrating End-to-End Developer Workflows with Agents: How to use autonomous assistants to speed up the ML lifecycle.
- Fine-Tuning Gemini for Industry-Specific Use Cases: Technical session on customizing foundation models for retail or finance.
- Scaling Evaluations for Complex AI Use Cases: Methods for testing agent performance before they hit production.
- Advanced Prompt Engineering for Multi-Step Reasoning: Improving the logic and reliability of model outputs.
Appropriate Track: Applied AI. This track bypasses theory to focus on the practical application of models in business settings.
DevOps and Platform Engineers
Efficiency is the goal for platform teams. This means automating everything from CI/CD to resource monitoring.
- CI/CD with AI Workflows: Integrating intelligent agents into the deployment pipeline to catch errors early.
- Cost Optimization for High-Scale AI Infrastructure: Specific techniques for keeping cloud bills under control during training and inference.
- Observability in the Age of Autonomous Agents: How to monitor systems when the logic is no longer hard-coded.
- Kubernetes for AI: Managing Large-Scale Clusters: A deep dive into the latest features of GKE for model serving.
- Automating Infrastructure Design with AI Agents: Moving toward a world where the platform builds itself.
Appropriate Track: DevOps, IT Ops, & Platform Engineering. This track is the home for anyone focused on the operational stability of cloud systems.
Software Engineers
Developers are now builders of agents rather than just writers of code.
- Developer Keynote: The primary event for seeing new APIs and IDE integrations in action.
- Building Conversational Agents on PostgreSQL and MySQL: How to add intelligent interfaces to existing database applications.
- Developer’s Guide to Building ADK Agents with Skills: A look at the latest SDKs for expanding agent capabilities.
- Beyond the Hype: Orchestrating Workflows with Agents: Practical session on using AI to handle repetitive coding tasks.
- Integrating Gemini into the Modern Web Stack: How to use vertex-side models to power front-end experiences.
Appropriate Track: App Dev. This track is designed for the hands-on practitioner writing the next generation of software.
IT Infrastructure and Operations Leaders
Operations leaders must ensure the lights stay on while the underlying technology changes completely.
- Infrastructure Modernization: Beyond the Basics: A look at the hardware and networking shifts required for 2026.
- Managing Enterprise Transformation in the AI Era: Strategies for upskilling teams and shifting operational mindsets.
- Hybrid Cloud Strategies for AI Deployment: How to balance on-premises security with cloud-based intelligence.
- Optimizing Reliability in Large-Scale AI Training: Technical talk on checkpointing and fault tolerance.
- Vendor Ecosystem Insights: Choosing the Right Partners: A guide to the third-party landscape surrounding Google Cloud.
Appropriate Track: IT Managers & Business Leaders. This track balances technical feasibility with organizational management.
Security Engineers and Architects
Agentic AI expands the attack surface in ways traditional security controls weren’t designed to handle.
- The Agentic SOC: Separating Hype from Reality: Practical application of AI in security operations centers for threat detection.
- Beat Fraud at its Own Game with an AI Shield: Using real-time models to identify and block fraudulent transactions.
- Securing the AI Supply Chain: How to protect models, data, and prompts from tampering or leakage.
- Zero-Trust Architecture for Decentralized Agents: Applying identity-based security to autonomous systems.
- Google SecOps: Advanced Threat Hunting with Gemini: Hands-on look at how AI accelerates the investigation of security incidents.
Appropriate Track: Security. This track is the priority for anyone responsible for protecting enterprise data.
AI/ML Researchers
Research and development teams should focus on sessions that push beyond current production capabilities.
- Multi-Step Reasoning Systems: The Next Evolution: Deep dive into the internal logic of the latest Gemini iterations.
- Small Mighty Teams: Operating Agentic AI at Scale: Research-led look at the most efficient organizational models for AI labs.
- Advances in Continuous Checkpointing and Reliability: The latest findings on improving training efficiency for massive models.
- Ethics and Governance in Autonomous Systems: A look at the research behind making AI safe and unbiased.
- Customizing Open Models for Specialized Research: How to use Google’s open-source tools for proprietary discovery.
Appropriate Track: Vertex AI. This track offers the most depth for those building or refining the models themselves.
Enterprise Architects
Enterprise architects must ensure that new AI tools integrate with the existing software stack.
- Building Your Unified AI Data Hub: A strategic look at integrating disparate data sources for a single intelligence layer.
- Enterprise AI Governance: A Framework for Success: How to establish rules for AI usage across a global organization.
- Chat with Your Data Using Gemini: Real-world examples of how to make enterprise knowledge accessible via natural language.
- Modernizing Platforms for Commerce: Inside the Retail Transformation: Case study on updating monolithic systems for the modern era.
- AI Integration into Enterprise Applications: A technical guide to the “last mile” of AI deployment.
Appropriate Track: Infrastructure Architects & Admins. This track focuses on the long-term stability and integration of the entire IT estate.
Operationalizing Intelligence
Companies are now focused on the practical mechanics of running agentic systems at scale. This involves a shift in how we think about security, data engineering, and even the role of the developer. Each role listed in this Google Cloud Next 2026 guide faces a different set of challenges, but the underlying requirement is the same: Move from experimentation to production.
Success in this environment requires a deep understanding of the infrastructure, the data pipelines, and the security protocols that make these systems viable. Use your time in Las Vegas to build the connections and gain the specific technical knowledge needed to lead your organization through this shift.
