Learn Agentic AI: Here's Where I'd Actually Start
Most advice I see for junior developers getting into agentic AI falls into one of two camps: either it's way too abstract ("just understand the theory first!") or it throws you straight into a full LangChain tutorial that assumes you already know what a vector store is. Neither is great. Here's what I'd actually tell someone starting from scratch, based on what's worked for me and what I've watched teammates struggle through. Get the Mental Model Right Before You Touch Any Framework This is the step most people skip, and it costs them weeks. An agentic system is not a chatbot with extra steps. The fundamental difference is that the LLM is making decisions about what to do next, not just generating text in response to a prompt. It might call a tool, inspect the result, decide the result wasn't good enough, call a different tool, and then synthesize everything into a response. That loop, where the model is reasoning about its own next action, is what makes these systems both powerful and genuinely hard to debug. When I first started building agents, I kept thinking of the LLM as the "smart part" and everything else as plumbing. That framing broke me almost immediately. The agent's behavior is emergent. It comes from the combination of the model, the tools you give it, the system prompt, the memory you provide, and the order in which things happen. None of those pieces alone explains what you'll see at runtime. Spend a day just reading about the ReAct pattern (Reasoning + Acting). It's the conceptual backbone of most agent frameworks. The original paper is on arXiv and it's actually readable, which is not something I say about most ML papers. Once you understand why the loop works the way it does, a lot of the framework decisions you'll encounter start making sense. An ATS agent is a sharp example of why this mental model matters. It's not just retrieving documents. It's making sequential judgment calls: should it screen the resume against the job description first, or check the candidate's location eligibility? If the job requires a specific certification, does it short-circuit the rest of the pipeline? When it finds ambiguous employment dates, does it flag for human review or proceed with a best guess? Every one of those branches is a potential failure point, and none of them show up cleanly in a log file. Pick One Framework and Go Deep, Not Wide You'll hear about LangChain, LlamaIndex, CrewAI, AutoGen, Spring AI, LangGraph, and about fifteen others depending on which corner of the internet you're in. Don't try to learn them all. Pick one, build something real with it, and understand its tradeoffs before you look at anything else. My honest recommendation if you're on the JVM: Spring AI 1.0 with Spring Boot 3.5 and Java 24. The ChatClient API is clean, the @Tool annotation makes wiring up agent tools genuinely straightforward, and the whole thing integrates naturally with the Spring ecosystem you probably already know. The community is smaller than Python's LangChain world, but for production JVM work it's the right call, and it's moving fast. Here's the simplest possible Spring AI agent that does something real, wired up for an ATS-style use case. First, the dependencies: <!-- pom.xml --> <properties> <java.version>24</java.version> <spring-ai.version>1.0.0</spring-ai.version> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-openai-spring-boot-starter</artifactId> <version>${spring-ai.version}</version> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-actuator</artifactId> </dependency> </dependencies> Now the tools. In Spring AI, a tool is just a Spring component with @Tool -annotated methods. The method's Javadoc becomes the description the model reads when deciding which tool to call, so write it carefully: // Java 24 - using records for clean data modeling record RequisitionResult(String requisitionId, String title, String department) {} record EligibilityResult(String candidateLocation, String requisitionId, boolean eligible) {} @Component public class AtsTools { @Tool(description = """ Search for open job requisitions matching a candidate's skills or query. Returns a list of matching roles with their requisition IDs. """) public List<RequisitionResult> searchOpenRequisitions(String query) { // Stub: replace with a real ATS database or API call return List.of( new RequisitionResult("req-1042", "Senior Backend Engineer", "Engineering"), new RequisitionResult("req-1078", "Platform Engineer", "Infrastructure"), new RequisitionResult("req-1091", "Staff SRE", "Operations") ); } @Tool(description = """ Check whether a candidate's location meets the work authorization requirements for a specific job requisition. Call this only after confirming a matching requisition exists. """) public EligibilityResult checkLocationEligibility( String candidateLocation, String requisitionId) { // Stub: replace with real eligibility logic return new EligibilityResult(candidateLocation, requisitionId, true); } } And the orchestration layer, where ChatClient runs the agent loop: @Service public class AtsAgentService { private final ChatClient chatClient; public AtsAgentService(ChatClient.Builder builder, AtsTools atsTools) { this.chatClient = builder .defaultSystem(""" You are a candidate screening assistant for a recruiting team. Always search for matching requisitions before checking eligibility. If a tool returns no results, say so. Do not retry more than once. """) .defaultTools(atsTools) .build(); } public String screen(String userQuery) { return chatClient.prompt() .user(userQuery) .call() .content(); } } This is a real, runnable agent loop. The model decides whether to call a tool. If it does, the tool runs, the result goes back into the conversation, and the model reasons again. If it doesn't need another tool call, you get a final answer. That's the whole thing. Build variations of this for a couple of weeks. Swap in different tools. Add a memory component that carries candidate context across multiple screening steps using Spring AI's ChatMemory abstraction. Break it intentionally and watch what happens. Learn to Read What the Agent Is Actually Doing This is where most junior people hit a wall. The agent produces a wrong answer, and they have no idea why. Was it the prompt? The tool output? A bad model decision halfway through the loop? You need observability from day one. Not as an afterthought. Spring Boot 3.5 ships with Micrometer Tracing on the classpath, and it integrates with OpenTelemetry out of the box. Add the bridge and an exporter, and you get automatic trace propagation through your agent almost for free: <dependency> <groupId>io.micrometer</groupId> <artifactId>micrometer-tracing-bridge-otel</artifactId> </dependency> <dependency> <groupId>io.opentelemetry</groupId> <artifactId>opentelemetry-exporter-otlp</artifactId> </dependency> # application.yml management: tracing: sampling: probability: 1.0 otlp: tracing: endpoint: http://localhost:4317 spring: ai: openai: api-key: ${OPENAI_API_KEY} chat: options: model: gpt-4o For local development I point this at a Grafana instance running in Docker. The ability to query spans by tag, like which tool the agent called or what the eli...