#agents
11 articles tagged agents, from AI, Spring AI, Learning. Latest: “Agentic AI Explained: From Prompt to Autonomous Systems”.
Agentic AI Explained: From Prompt to Autonomous Systems
What agentic AI actually is, how the agent decision loop works, single vs multi-agent systems, the enterprise stack, real use cases, and the risks nobody skips.
I Rebuilt Our AI Agents on Kafka Instead of REST — Here's the Full Java Architecture
REST between AI agents works until agent #4 shows up. The full Java/Spring Kafka topic design, the ReAct loop as a state machine, and a free audit trail.
Building AI Skills: GitHub Copilot Extensions, Claude Tools, and Reusable Agent Capabilities
One skill, three platforms. Building a custom capability for GitHub Copilot, Claude, and your own AI agents — with real code and a decision framework.
Agent Memory Patterns: How to Give Your LLM Application a Brain That Persists
Your LLM forgets everything once the request ends. How production systems build short-term, long-term, and semantic memory — with Spring AI and pgvector.
MCP vs Function Calling: What's the Difference (and When to Use Each)
Model Context Protocol and function calling both give LLMs access to tools, but solve different problems — a clear comparison and guidance on when to use each.
Context Engineering: The Real Skill Behind Reliable LLM Apps
What you put in the context window matters more than prompt wording. A practical guide to context engineering — the budget, techniques, and failure modes.
Tool Calling with Spring AI
A hands-on guide to Spring AI tool calling — define @Tool methods, wire them into ChatClient, and let Claude safely call your Java services.
Building an MCP Server with Spring AI
Most MCP content is Python/TypeScript. Here's how to build an MCP server in Java with Spring AI — expose your services as tools any MCP client can use.
Introduction to Anthropic MCP (Model Context Protocol)
A practical guide to the Model Context Protocol — what it is, how it works, and why it matters for building interoperable enterprise AI systems.
Building Enterprise AI Agents
Architecture patterns and best practices for building production-grade AI agents that scale — from orchestration to observability.
Securing LLM Apps: Guardrails for Production
LLM features open new attack surfaces — prompt injection, data leakage, unsafe tool use. A practical guardrails checklist for shipping AI to production safely.