On-Demand Webinar
Beyond Chatbots: Orchestrate Your AI Workforce with Databricks Agent Bricks & MLflow
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Key Takeaways
Learn how Agent Bricks accelerates multi-agent development and deployment on Databricks.
Explore architectural best practices for building secure, observable, and collaborative agents.
Understand how MLflow, Unity Catalog, and Model Serving work together for full lifecycle governance.
Discover how to move from experimental agents to trusted, production-grade automation.
Build a smarter, connected AI ecosystem across your data and tools.
Is your AI strategy still limited to chatbots and isolated assistants? Single agents are useful—but they can’t coordinate complex, multi-step decisions on their own.
The next leap forward is the AI workforce: specialized agents that collaborate, delegate, and act intelligently across your data and tools.
Watch this on-demand webinar, as we explore how Databricks Agent Bricks and MLflow 3.0 together enable you to build, manage, and govern multi-agent systems natively within Databricks Lakehouse. Learn how Agent Bricks serves as your AI collaboration hub, and how MLflow delivers complete observability and governance—so you can move from simple question-answering bots to enterprise-ready autonomous systems that drive real business outcomes.
Agenda
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From Solo Agent to AI Squad
- Understanding the “complexity ceiling” of single-agent systems.
- The multi-agent paradigm: specialized agents for planning, execution, and validation.
- Architecting for coordination and collaboration.
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Building Your AI Workforce with Agent Bricks
- Common agent applications supported by Agent Bricks
- Simplifying communication and task orchestration with Agent Bricks supervisors.
- Designing multi-agent workflows for complex decision-making.
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Supercharging with Secure Tool Integration
- Enabling agents to interact safely with enterprise systems via Unity Catalog Function Connectors.
- Managing secure access to data and APIs for contextual intelligence.
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Mission Control: Governance with MLflow
- Using MLflow Evaluations & Traces to visualize, debug, and improve multi-agent performance.
- Establishing traceability, compliance, and confidence in agent-driven automation.
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