Mario Fusco

Agentic AI Systems for Critical Workloads

Traditional unit tests are useless against agentic AI. Learn to build and test reliable systems for critical Java workloads using a new probabilistic framework.

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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Exploring frameworks for building agentic AI applications in Java
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Supercharge Agentic AI Apps: A DevEx-Driven Approach to Cloud-Native Scaffolding

Integrating generative AI into Java applications with LangChain4j
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Understanding LangChain4j for Java AI applications
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Understanding the core components of an AI agent
00:05 MIN

Understanding the core components of an AI agent

Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure

Deconstructing the modern agentic systems stack
12:23 MIN

Deconstructing the modern agentic systems stack

The State of GenAI & Machine Learning in 2025

Tracing the evolution from LLMs to agentic AI
02:48 MIN

Tracing the evolution from LLMs to agentic AI

Exploring LLMs across clouds

Defining the agent's behavior with a Java interface
13:27 MIN

Defining the agent's behavior with a Java interface

Supercharge Agentic AI Apps: A DevEx-Driven Approach to Cloud-Native Scaffolding

Demo of an AI assistant using LangChain4j and Quarkus
24:34 MIN

Demo of an AI assistant using LangChain4j and Quarkus

Create AI-Infused Java Apps with LangChain4j

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