#agents
9 articles tagged agents.
Building AI Skills: GitHub Copilot Extensions, Claude Tools, and Reusable Agent Capabilities
One skill, three platforms. A complete guide to building a custom capability for GitHub Copilot, Claude, and your own AI agents — with real code, architecture diagrams, and a decision framework for when to use each.
Agent Memory Patterns: How to Give Your LLM Application a Brain That Persists
Your LLM forgets everything the moment the request ends. Here's how production systems build short-term, long-term, and semantic memory — with Spring AI, Redis, and pgvector examples.
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 they solve different problems. A clear comparison with guidance on when to use which.
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.