Intelligence Prompting

Reviewed topic hub

AI Agents: prompts, architecture, tools, 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.

  • AI agent prompts and task contracts
  • Agent architecture and orchestration
  • Tool use, memory, and state
  • Evaluation, observability, and safety

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.

01

22 min read

MCP vs A2A: How AI Agent Protocols Work Together in 2026

Compare MCP and A2A, see where each protocol fits, and use a practical architecture, security checklist, and eight prompts to design agent systems.

  • MCP connects an AI application to tools, data, prompts, and other context; A2A lets independent agents discover one another, exchange messages, manage tasks, and return artifacts.
  • The protocols are complementary. A multi-agent product can use A2A at the agent boundary and MCP inside each agent to reach its approved tools and information.
  • Protocol adoption does not create trust by itself. Identity, authorization, data classification, approval gates, observability, and evaluation remain application responsibilities.
Read the field guide
02

28 min read

AI Agent Prompts: 25 Templates for Reliable Workflows in 2026

Copy 25 practical AI agent prompts for research, planning, automation, analysis, tool use, and verification—with safeguards for reliable workflows.

  • Reliable AI agent prompts define the goal, evidence, allowed tools, decision boundaries, stop conditions, and a verifiable final deliverable.
  • The 25 copy-ready templates cover research, planning, automation, analysis, tool use, and verification without depending on one AI model or platform.
  • Autonomy should match consequence: use read-only access and explicit approval by default, then expand permissions only when the workflow proves reliable.
Read the field guide
03

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.
Read the field guide
04

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.
Read the field guide