Mario Fusco
Agentic AI Systems for Critical Workloads
#1about 2 minutes
Why Java is a strong choice for enterprise AI applications
LangChain4j brings AI capabilities to Java, which is ideal for building enterprise-grade systems that require transactions, observability, and security.
#2about 3 minutes
Understanding the core components of agentic AI systems
Agentic AI systems consist of a core LLM, tools, memory, and orchestration, with the key distinction being between programmatic workflows and autonomous agents.
#3about 3 minutes
Practical challenges when building with local LLMs
Developing with local LLMs involves significant trial and error in model selection and prompt engineering, and requires handling issues like tool hallucination.
#4about 5 minutes
Building predictable AI systems with the workflow pattern
The workflow pattern uses programmatic code to orchestrate specialized agents in sequences, parallel tasks, or a mixture of experts for predictable outcomes.
#5about 6 minutes
Strategies for testing non-deterministic AI applications
Testing LLM-based systems requires new approaches like using sample-based evaluation, custom scoring functions, and strategies such as cosine distance or LLM-as-a-judge.
#6about 7 minutes
Comparing the workflow pattern to the agent pattern
While workflows offer predictability and easier debugging, the agent pattern provides greater flexibility by allowing agents to autonomously decide which tools to use.
#7about 3 minutes
Creating advanced agents that use external tools
Agents can autonomously combine LLM capabilities with external tools like web services or search engines to accomplish complex, multi-step tasks.
#8about 2 minutes
The future of agent orchestration in LangChain4j
Upcoming features include integration with the AITO protocol and a new programmatic API for composing complex agent interactions like sequences and loops.
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Matching moments
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Exploring frameworks for building agentic AI applications in Java
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02:48 MIN
Tracing the evolution from LLMs to agentic AI
Exploring LLMs across clouds
02:10 MIN
Understanding the shift to the agentic era
Event-Driven Architecture: Breaking Conversational Barriers with Distributed AI Agents
15:55 MIN
Shifting focus from standalone models to complete AI systems
Navigating the AI Revolution in Software Development
05:05 MIN
Transitioning from monolithic agents to multi-agent systems
Beyond Prompting: Building Scalable AI with Multi-Agent Systems and MCP
00:21 MIN
Why AI agents fail in production environments
The AI Agent Path to Prod: Building for Reliability
00:05 MIN
The core challenge of scaling AI agent communication
Event-Driven Architecture: Breaking Conversational Barriers with Distributed AI Agents
04:35 MIN
Exploring common AI application patterns
Building AI Applications with LangChain and Node.js
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