Architecture behind production AI agents
Most teams can build an AI agent. Far fewer can deploy one that is reliable, observable, and cost-effective in production.
In this live session, we’ll build a compliance audit AI agent based on a real-world case study using the Nebius Agents Blueprint. Then we’ll replace the standard agent configuration with LangChain Deep Agents optimized for NVIDIA Nemotron 3 Ultra and demonstrate how to achieve frontier-level quality at roughly one-tenth of the cost.
What you’ll learn
- Deploy an AI agent using the Nebius Agents Blueprint
- Build with LangChain Deep Agents, Tavily and NVIDIA Nemotron 3 Ultra without model fine-tuning
- Compare an open-model architecture with a frontier-model approach to understand the trade-offs in quality and cost
- Add production capabilities including grounding, retrieval, observability, and simulation testing with Tavily, Pinecone, LangSmith, and Snowglobe
- Reproduce the complete implementation using the architecture and code shared during the session
Who should attend
- AI/ML engineers building agents who want a production-ready architecture
- Platform and infrastructure teams responsible for deploying AI systems
- Technical leaders evaluating open-model agents versus closed frontier models for cost, control, and reliability
- Teams moving from prototypes to production and looking for practical architecture patterns
Fill out the form to register and get the recording
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