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World Congress 2026 Europe • Jul 9, 2026 • Session details

AI in Production: applied AI & enterprise use cases

Mohak Chadha

Scaling enterprise AI requires a shift from mere prompts to strict harness engineering. Learn to transform isolated models into secure, autonomous agents using deep observability.

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#1 about 3 min

Shifting focus from isolated models to enterprise AI systems

Building real-world AI systems requires moving beyond single prompt models to continuous agent loops.

#2 about 3 min

Managing state and dynamic context in agent workflows

Agents function as reasoning engines requiring dynamic assembly of working context at each step.

#3 about 2 min

Controlling model execution with dedicated runtime harnesses

A dedicated harness manages tool invocation, context updates, and failure recovery securely.

#4 about 3 min

Infrastructure requirements for deploying production enterprise agents

Moving to production requires connecting agents to secure infrastructure, observability tools, and continuous evaluation frameworks.

#5 about 2 min

Engineering lifecycle coverage with the NeMo agent toolkit

The open-source toolkit provides guardrails, telemetry, and debugging capabilities across multiple orchestration frameworks.

#6 about 2 min

Evaluating agent task execution and workflow performance

Comparing models and retrieval strategies requires automated metrics capturing latency, token usage, and accuracy.

#7 about 2 min

Royal Bank of Canada document processing capabilities deployment

Scaling financial data processing pipelines relies on orchestration engines to manage structured and unstructured data.

#8 about 2 min

Decomposing enterprise research tasks into multi-agent workflows

Complex research questions demand planner capabilities that delegate tasks to specialized sub-agents and preserve evidence.

#9 about 3 min

Blueprint architectures for scalable enterprise intent routing

Intent-based routers combined with retrieval models and secure execution sandboxes automate comprehensive research investigations.

#10 about 3 min

Autonomous agent resolution for enterprise service operations

Specialized AI agents triage tickets, parse logs, and implement solutions to drastically reduce engineering backlogs.

#11 about 3 min

Transitioning an agentic workflow to enterprise video intelligence

Integrating vision language models and embedding search enables continuous monitoring and alert generation for video streams.

#12 about 3 min

System design and orchestration for video AI agents

Deploying Cosmos 3 models on Kubernetes with elastic search infrastructure handles concurrent real-time intent routing.

#13 about 2 min

Logistics factory search via video analytics dashboard

Querying massive video data isolates exact timestamps of unrecorded events such as aisle obstructions using precise event chunking.

#14 about 3 min

Generating automated reports and reasoning about long videos

Asking direct questions to the video intelligence model generates multi-page safety reports from visual observations.

#15 about 2 min

Configuring specific real-time alerts for compliance monitoring

The intelligence layer identifies safety protocol violations instantly as they occur on a live application camera feed.

Matching moments

40 sec

Introduction to building real-world AI agent solutions

Dennis Zielke Dennis Zielke +1 · WWC 2025

2:52 min

Designing agentic AI solutions for the enterprise

Damir Dobric · Coffee With Developers

2:08 min

Navigating the components of the modern generative AI stack

Julián Duque Julián Duque · WWC 2025

2:46 min

Deploying AI agents for enterprise legacy code modernization

Neel Sundaresan Neel Sundaresan +1 · WWC Europe 2026

2:28 min

Introduction to building reliable AI agents in production

Max Tkacz Max Tkacz · WWC 2025

2:52 min

Scaling generative AI use cases across large enterprises

Mike Butcher Mike Butcher +3 · WWC 2024

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