7th Ann. MLOps World | GenAI Conference & Expo
About this Event
👋 Thank you for joining us here
The goal of the MLOps World | GenAI Summit 2026 is to help companies move from experimentation to production, where AI Agents and Agentic Workflows are transforming how teams build, scale, and operate AI.
This year’s flagship theme is AI Agents & Agentic Workforces, with a focus on:
- AI Agents for Developer Productivity: accelerating workflows and innovation
- AI Agents for Model Validation & Deployments: ensuring reliable systems in production
- Augmenting Agentic Workforces: scaling human + AI collaboration
- Agents in Production: lessons from real-world deployments
- Latest Trends in MLOps: staying ahead with evolving best practices
We’re a group of practitioners committed to sharing case studies, architectures, and proven strategies, no hype, no product pitches.
Just hard-earned lessons from teams deploying agents and agentic systems at scale.
Whether you’re exploring copilots, scaling agent stacks, or running multi-agent orchestration, this summit is designed to give you the insights and tools to succeed in production.
👉 Join us in Austin this October to connect with over 700+ AI engineers, platform teams, and leaders building the future of AI Agents in production.
- MLOps World Team
General Admission ticket include;
✅ Complete access to the Summit Day 1 (Nov 17th) and Day 2 (Nov 18th)
✅ Complete access to the bonus virtual talks and workshops on Nov 16th
✅ Access in-person talks, networking with food and drinks
✅ AI-Powered Desktop & mobile event app for online networking
✅ Conference parties
✅ Access to post-summit videos
MLOps World is an international community group of practitioners working to advance the science of deploying not only ML models but also agents and agentic workflows into live production environments. We explore everything, technical and non-technical, that goes into making these systems reliable and effective.
With an explorative approach, our initiatives address the needs of a community of over 18,000+ ML and AI practitioners, researchers, professionals, entrepreneurs, and engineers. We’re here to empower members, propel productionized AI, and shape the next generation of agent-driven systems.
Our gatherings and events aim to reimagine what it means to have a connected community, offering support, growth, and inclusion for all participants.
Nov 16 - Virtual Session
🕑: 10:20 AM - 10:50 AM
Scaling AgentOps: Observability, Safety and Control in Production AI
Host: Andy McMahon, Principal AI and MLOps Engineer, Barclays
Info: As organisations move from single models to connected agent systems, the challenge shifts to how agents interact, how they are evaluated, and how their behaviour can be monitored in real production environments.
This presentation explores what it takes to operationalise agentic AI in practice – from observability and evaluation to deploying agents safely and reliably across enterprise workflows.
Through real-world examples, we look at how teams are building responsible AgentOps frameworks to scale multi-agent systems in production while maintaining control, trust and measurable business impact.
🕑: 10:55 AM - 11:25 AM
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Host: Yegor Denisov-Blanch, Research Scientist, Stanford Unive
Info: Placeholder
🕑: 11:30 AM - 12:00 PM
Beyond Unit Tests: A digital-Twin Approach to AI Agent Evaluation
Host: Vicente Ruben Del Pino Ruiz, Sr Director AI & Data Engin
Info: Single-shot evaluation, the shape every current AI eval framework ships, is structurally blind to the failures that take agents down in production: patience that runs out at turn six, users who abandon silently, tool calls that lose conversation state, partial successes that masquerade as wins. This talk argues for a different shape, drawn from the digital twin literature and how the approach is already applied in aerospace, autonomous vehicles, and civil engineering: simulate the population of users your agent will meet, run them against the agent, watch what breaks before any human sees it.
Three take aways:
1. Why current AI agent evaluation cannot detect the failures that hit production. The structural reason prompt-and-grade testing (the shape every current framework ships) is blind to patience, abandonment, conversation state, and partial success.
2. What a realistic synthetic user population looks like, drawn from the digital twin literature in other engineering industries.
Nov 17
🕑: 10:55 AM - 11:25 AM
Intelligent PDLC: Building Trustworthy AI-Assisted Product Delivery
Host: Ashish Garg, Principal Product Manager, Walmart
Info: In large enterprises, work-intake is where strategy quietly goes to die. Requests arrive through multiple channels, context is incomplete, ownership is fuzzy, and dependencies stay invisible until they explode in the sprint. Leaders often reach for AI to “summarize everything,” but that first wave of copilots created a new problem: answers that sound confident while drifting away from reality—eroding trust, triggering governance pushback, and creating more churn than clarity.
This session is a practitioner blueprint for Intelligent PDLC: how to introduce AI into intake and planning in a way that teams can actually trust. Instead of focusing on model theory, we focus on the operating system around AI—how work gets structured, how ambiguity is handled, how evidence is captured, and how humans stay in the loop without slowing everything down.
You’ll learn patterns that consistently work in high-stakes environments: clarifying intent before generation, enforcing “evidence-first” responses
🕑: 10:55 AM - 11:25 AM
When Pricing, Catalog, and Compliance Collide: Building Multi-Agent AI Swarms
Host: Amit Kumar Padhy, Senior Computer Scientist II & Lead Ar
Info: Modern commerce platforms don't fail because of missing features, they fail at the seams.
A product is created in Catalog, but pricing is incomplete. Promotions don't qualify. Tax blocks specific regions. Localization lags. The system says ""launched,"" but the business knows it isn't. These aren't edge cases, they are the steady state of distributed commerce.
This session replaces traditional workflow orchestration with a multi-agent, swarm-based execution model powered by LLMs and agentic AI, coordinating Pricing, Catalog, Promotions, Tax, and Compliance in real time.
Agent Roles. Planner Agents decompose onboarding goals into executable plans using ReAct-style tool-aware reasoning and function calling. Domain Agents for Pricing, Catalog, and Compliance execute directly against APIs, Pricing Runtime, Offer Systems, Billing Preview, Tax engines. Validator Agents enforce policy rules, regional compliance, and pricing integrity at every step. A Coordinator Agent maintains shared state
🕑: 10:55 AM - 11:25 AM
Reducing Operational Toil with AI-Assisted Incident Response in Enterprise Kub
Host: Sai Joshitha Kathari, Senior Site Reliability Engineer
Info: Modern enterprise infrastructure environments generate massive volumes of operational telemetry, alerts, deployment events, and troubleshooting data, yet incident response workflows remain highly manual for many SRE teams. Engineers frequently spend valuable time correlating alerts, validating deployment health, searching fragmented documentation, and identifying root causes during high-pressure production incidents.
This session explores practical approaches for integrating AI-assisted operational workflows into Kubernetes-based enterprise environments to improve incident response efficiency and reduce operational toil. Drawing from real-world SRE experience supporting high-availability distributed systems in the fintech industry, the session examines how AI-assisted knowledge retrieval, alert correlation, deployment validation, and operational automation can improve reliability engineering workflows without introducing unrealistic “fully autonomous” operational assumptions.
🕑: 11:30 AM - 12:00 PM
Building a Multi-Agent Employee Performance Intelligence System
Host: Roopansh Bansal, Senior Software Engineer, Uber
Info: Modern employee performance workflows such as impact resumes, self-assessments, and manager reviews are fundamentally data synthesis problems. Contributions are distributed across systems like Jira, GitHub, documents, and feedback tools, yet employees are expected to consolidate this into structured, high-quality narratives under time pressure.
We present the design of a Performance Intelligence Platform, a multi-agent GenAI system that assists employees, managers, and review committees in generating performance artifacts while preserving human ownership of evaluation.
The system evolved from a GenAI-powered Impact Resume Builder that reduced drafting time by over 90%, into a generalized, multi-tenant platform supporting the full performance lifecycle.
At its core is a multi-agent orchestration layer:
1. A Supervisor Agent dynamically plans workflows based on user intent
2. Data collection agents aggregate employee artifacts across internal systems
3. Ranking and grouping agents pr
Nov 18
🕑: 10:55 AM - 11:25 AM
LLMs in Regulated Industries: What Every Data Scientist Needs to Know
Host: Chandni Bhatia, Vice President ( Quantitative Modeling
Info: Deploying LLMs in regulated industries requires a fundamental rethinking of how ML systems are designed, validated, and governed. In this session, I'll walk through the structural changes needed when building LLM pipelines that must satisfy regulatory frameworks like SR 11-7 — covering orchestration architecture, observability infrastructure, model risk controls, and documentation standards that go far beyond typical MLOps playbooks.
Attendees will walk away with:
A practical framework for evaluating LLM readiness for regulated production environments
Concrete MLOps patterns for auditability, explainability, and version control in compliance-driven settings
A checklist of the most common failure modes when deploying LLMs in regulated industries — and how to avoid them
🕑: 10:55 AM - 11:25 AM
Workflow-Based Orchestration Layer for Long-Running AI Agent Sessions
Host: Ankit Goyal, Principal Staff Software Engineer, LinkedIn
Info: To support complex autonomous tasks like model optimization and software engineering, we redesigned our AI infrastructure by shifting from stateless, request-response paradigms toward a model of managed, long-running execution. Structurally, this involved moving away from ad-hoc scripts toward a unified agentic harness where every session is treated as a durable, managed workflow. We implemented a decoupled sidecar architecture that effectively insulates core infrastructure from the unpredictable execution paths of autonomous agents, ensuring that identity, security, and network egress are managed through a hardened proxy rather than the agent itself
Attendees will walk away knowing how to architect a secure execution harness for autonomous agents, implement asynchronous human intervention models, and manage the lifecycle of persistent agentic sessions in high-scale production environments.
🕑: 10:55 AM - 11:25 AM
The Model Was Right. The Corrections Still Failed.
Host: Deji Andrew, Manager, Systems & Data Platforms, Niagara
Info: A LightGBM model was deployed across production lines to predict and correct overruns mid-job, reducing average overproduction from +24 cases to +2 on historical data. In pilot, the corrections worked: overruns vanished. But a closer look at the production data showed something different. When the order target was reduced by 21 cases, the line overproduced the new target by 18. The system was not being corrected - it was negotiating with the correction, regenerating overrun relative to whatever target the machine was now working toward. Compounding this: corrected jobs left no trace in the training data that a human had intervened, meaning the model, on retraining, would learn to stop flagging the problem it had been built to solve. This talk walks through the pilot data, explains why conservatism in the loss function wasn't enough, and argues that the real gap isn't in the model - it's in the experiment infrastructure that doesn't exist between a prediction and a PLC.
🕑: 11:30 AM - 12:00 PM
Closing the Gap Between What AI Is Trained On and What Users Actually Need
Host: Jazmia Henry, Research Scientist, University of Oxford
Info: There's a gap between researcher-crafted evaluation frameworks that capture model performance and benchmarks versus how end users actually use AI products. This gap is being exploited in ways that render traditional reward models useless.
🕑: 11:30 AM - 12:00 PM
The Agentic Command Center: Architecting a 'Single Pane of Glass' for Secure E
Host: Archit Agarwal, Senior Manager of ML/AI Engineering, McD
Info: Drawing from hands-on work with off the shelf tools and custom AI tools, we will showcase how to move from ""isolated pilots"" to an Agentic Command Center. We will explore:
Secure Infrastructure: How to set up a ""single pane of glass"" that provides centralized governance and observability across diverse enterprise agents.
Practical Agentic Governance: A deep dive into the ""Secured Agent Sharing"" framework, demonstrating how teams can safely publish, discover, and reuse specialized agents without compromising data perimeter security.
Attendees will leave with a practical roadmap for transition from ""Agent Sprawl"" to a governed, scalable AI ecosystem.
🕑: 12:05 PM - 12:35 PM
From Prompt to Privileged Action: Identity and Audit Controls for Enterprise A
Host: Siddharth Jain, AI Engineering Manager, OpenAI
Info: Teams often give an agent a service credential, add a human approval step, and call the workflow governed. That design breaks down when the agent can revise payloads, retry writes, chain tools, or act across systems with different permission models. The result is an accountability gap: the organization can see that a service account acted, but not necessarily who authorized the business intent, which payload was approved, whether a retry duplicated work, or what changed between proposal and execution.
This session presents a production control model for agent workflows that make consequential writes. It shows how to separate the business-intent identity from the concrete operation; classify tools by impact; issue short-lived, least-privilege credentials only after validation; bind human approval to a canonical payload hash and policy version; execute through controlled services with idempotency keys; and reconcile external state before declaring success. It also covers the evidence
Where is it happening?
Event Location & Nearby Stays:
USD 553.41 to USD 564.50
