Expert topic hub

AI Agents: architecture, tools, memory, evaluation, and governance

An AI agent is more than a chatbot with a longer prompt. It combines a model with instructions, tools, state, decision logic, and evaluation. This hub explains the architecture without hype and helps teams decide when an agent is justified.

Agent architecture and orchestration
Tool use, memory, and state
Evaluation, observability, and safety
Human approval and production governance

Learning path

Build understanding in the right order.

Start with the system model, then move into design, evaluation, and production controls. Each article links concepts to decisions.

11 min read

What is an AI agent? A practical architecture guide

A clear explanation of models, tools, memory, planning loops, guardrails, and the difference between agents and ordinary workflows.

An agent combines a model with tools, state, control logic, and evaluation.
Autonomy should be limited by risk, reversibility, and clear approval boundaries.
A deterministic workflow is often better when the process and decisions are already known.

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12 min read

How to evaluate AI agents before production

Build a test set, score trajectories, inspect tool use, measure business outcomes, and define safe release gates.

Evaluate decisions and tool calls, not only the final text.
Separate offline test results from production business outcomes.
Release gates should cover quality, safety, cost, latency, and recovery.

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