AI
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Software Development
AI Agent Orchestration Workflows, Routing, and Multi-Agent Systems
AI agents can reason, use tools, retrieve information, and execute tasks. However, complex applications require more than individual agents. They…
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Software Development
LLM evaluation: Test and improve AI quality
Large language models (LLMs) can generate human-like responses, but fluent output does not necessarily mean correct or reliable output. An…
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Software Development
AI Agent Security Guardrails: Tool Permissions and Human-in-the-Loop Controls
A plain LLM call is low-risk — worst case it returns a bad sentence. An agent is different: it can…
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Software Development
AI SDLC Explained: A Practical Guide to Building Agent Skills
The AI software development lifecycle (AI SDLC) extends familiar software engineering practices to systems that use models and agents. Alongside…
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Software Development
Semantic Caching for Large Language Models
Large Language Models (LLMs) have transformed the way applications process and generate natural language. They are widely used in chatbots,…
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Software Development
Design Patterns for AI Agents
AI agents extend language models from simply generating responses to carrying out goal-oriented workflows. By combining reasoning, tool use, feedback,…
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Enterprise Java
Long-Term Memory in Spring AI with AutoMemoryTools
AutoMemoryTools lets a Spring AI agent keep useful facts between sessions without treating the whole conversation transcript as permanent context.…
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Software Development
What is vLLM? A Quickstart Guide
Large language models have become easier to integrate into applications, but running them efficiently is still a major engineering challenge.…
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Enterprise Java
How to Switch Between AI Models Automatically
AI applications do not always need the same language model for every request. A simple factual question may only require…
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