Dennis Zielke & Rene Pajta
Building Blocks for Agentic Solutions in your Enterprise
#1about 2 minutes
Moving agentic AI from proof of concept to production
Enterprises struggle to productionize AI agents due to challenges in communicating value, selecting stable technology, and ensuring legal compliance.
#2about 3 minutes
A three-pillar strategy for enterprise AI implementation
A successful enterprise AI strategy requires aligning business outcomes with technology choices and operationalizing development through standardized processes.
#3about 4 minutes
The architectural layers of an enterprise agentic platform
An agentic platform is built on layers including user experience, a runtime, shared memory, and registries for discovering tools and other agents.
#4about 2 minutes
Designing a multi-layered memory strategy for AI agents
A robust memory strategy involves managing session-based context, persistent agent knowledge, and long-term organizational memory to enable hyper-personalization.
#5about 3 minutes
Four patterns for orchestrating multi-agent collaboration
Agents can collaborate through direct connections, as orchestrated tools, within a sequential workflow, or via a decoupled event-driven architecture.
#6about 2 minutes
Implementing human-in-the-loop for agent oversight
Semi-autonomous agents require human-in-the-loop processes for critical decision-making, with interventions designed for the user's specific work context.
#7about 5 minutes
Establishing agent identity for governance and security
Managing agents at scale requires establishing a clear identity for each agent to govern permissions, audit actions, and secure interactions.
#8about 2 minutes
Understanding the maturity levels of agentic AI
The evolution of agentic AI progresses from simple chatbots to semi-autonomous agents with human oversight, which is the current focus for delivering business value.
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Q&A on AI adoption, tools, and challenges
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Understanding the shift to the agentic era
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Defining agentic AI and its role as a colleague
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€52K
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