AI Engineering · Phase 3
Go from LLM calls to autonomous systems: ReAct, plan-and-execute, reflection, agent memory, multi-agent orchestration, evaluation and guardrails.
A clear, hype-free explanation of agentic AI: how it differs from generative AI, the five components of an agent, when autonomy is worth it, and when a plain workflow is the better engineering choice.
Read tutorialThe core agent architecture patterns explained with Java: ReAct, plan-and-execute, reflection, orchestrator-worker and routing — when to use each, and why simpler is usually better.
Read tutorialHow agents use tools well: designing tool schemas, dynamic tool selection, composing tools into workflows, error recovery, and keeping the tool set small enough to choose from.
Read tutorialHow agents plan and reason: task decomposition, hierarchical planning, chain-of-thought and tree-of-thought — with Java examples and honest guidance on when planning helps.
Read tutorialHow agent memory works beyond a chat window: working, episodic and semantic memory, vector-based recall, memory consolidation, and implementing persistent agent memory in Java.
Read tutorialBuilding multi-agent systems in Java: orchestrator-worker coordination, agent handoffs, communication protocols and conflict resolution — and the honest case for when one agent is better.
Read tutorialA practical comparison of agent frameworks for Java developers: LangChain4j, Spring AI, and how the Python ecosystem (LangGraph, CrewAI, AutoGen) compares — plus when to use no framework at all.
Read tutorialDesign autonomous coding agents in Java: code generation with verification, review agents that bias for precision, refactoring and test-generation agents — with the guardrails they need.
Read tutorialHow to evaluate and test AI agents: trajectory analysis, benchmarking, hallucination detection, outcome verification and human-in-the-loop evaluation — with Java patterns.
Read tutorialAdvanced RAG where the model controls retrieval: self-RAG, corrective RAG, adaptive retrieval and query planning — when to let an agent decide whether and what to retrieve, in Java.
Read tutorialDesign human-in-the-loop AI systems in Java: approval flows for agent actions, escalation patterns, confidence thresholds and feedback loops — how to deploy autonomy without accepting unbounded risk.
Read tutorialTake agents to production: per-run budgets and step caps, guardrails, durable execution, scaling on the JVM, cost control and the operational patterns that keep agents from causing incidents.
Read tutorialBuild responsible AI agents: managing bias, ensuring transparency and accountability, designing for contestability, and the engineering practices that make agents safe and fair.
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