AI agents do not fail like ordinary request-response applications. Learn how to combine logs, traces, metrics, evaluations, and alerts to understand each agent run from user request to final outcome.
AI Agent Observability: Logging, Tracing, and Monitoring Read Post »
AI agents become risky when model output can trigger real actions without a strong application security boundary. This architecture-first guide explains how to combine least-privilege permissions, per-tool authorization, deterministic guardrails, human approvals, input and output validation, and end-to-end auditability.
AI Agent Security: Permissions, Guardrails & Safe Tool Use Read Post »
LLM evaluation is more than checking whether an answer sounds correct. Learn how to test AI agent responses, tool calls, RAG grounding, safety, latency, and cost with a repeatable evaluation pipeline.
LLM Evaluation: How to Test AI Agent Response Read Post »
Embeddings explained from an architecture-first perspective: learn how embedding models convert content into vectors, where semantic retrieval fits in RAG and AI-agent systems, and what engineers must evaluate before production.
Embeddings Explained for AI Agents and RAG Read Post »
Vector databases for AI agents are often described as the component that gives an agent “memory.” That description is convenient,
Vector Databases for AI Agents: Architecture Guide Read Post »
RAG for AI agents provides controlled access to private, current, and authoritative business information. This architecture-first guide explains retrieval, reranking, security, citations, evaluation, and production design.
How RAG Helps AI Agents Use Your Own Data Read Post »
AI agents cannot run dependable workflows on prose alone. Learn how JSON schemas, validation, retries, and typed application boundaries turn an LLM response into data your software can safely use.
Structured Output in AI Agents: Why JSON Matters Read Post »
Tool calling in AI agents is the mechanism that lets a large language model request work from software outside the
Tool Calling in AI Agents Explained Read Post »
Agentic AI systems work toward defined goals through controlled, multi-step workflows. Learn how they plan, select approved tools, maintain task state, evaluate results, and take bounded actions—and how agentic AI differs from generative AI, individual AI agents, copilots, chatbots, and traditional automation.
What Is Agentic AI? How It Works and Where It Is Used Read Post »
Model Context Protocol, or MCP, gives AI applications a standardized way to connect with external tools, resources, prompts, APIs, documents, databases, and business systems. This architecture shows how an MCP host, client, and server work together while authentication, authorization, consent, validation, and logging remain essential security responsibilities.
What Is MCP? Model Context Protocol Explained Simply Read Post »
