Explainer
How Do AI Agents Actually Work?
Follow the agent loop from goal and tool choice to observation, verification and a deliberate stopping decision.
3 min readAI AGENTS & AUTOMATION
From answers to action. Keep control.
Understand how agents choose and use tools, then design workflows that act, observe, verify and stop within clear boundaries.
A CONTROLLED ACTION LOOP
Give every action a reason and a boundary.
An AI agent does more than produce an answer. It interprets a goal, chooses a next step, uses tools, observes the result and decides what follows. That flexibility can help with changing work, but it also creates new ways to fail.
Use one loop throughout this collection: GOAL → UNDERSTAND → CHOOSE → ACT → OBSERVE → VERIFY → CONTINUE / STOP. Four boundaries surround it: CAPABILITY, PERMISSION, COST and HUMAN OVERSIGHT.
Start with the foundational explanation of an AI agent, or connect agent design to the existing cost and performance framework.
01 / Start here
What makes a system an agent?
Begin with the action loop and the practical differences between assistants, workflows, automations and agents.
02 / Understand
Which parts turn a goal into action?
Examine instructions, models, tools, memory, state and verification before choosing an architecture.
Comparison
AI Agent vs AI Assistant vs Traditional Automation
Distinguish conversational assistants, fixed automation, AI-enabled workflows and agents by who chooses the next action.
2 min readExplainer
The Anatomy of an AI Agent
Map the model, instructions, tools, context, state, memory, permissions, verification and stopping rules around an agent.
2 min readPractical guide
When Should You Use an AI Agent—and When Should You Not?
Decide whether a task earns agentic flexibility or should remain a prompt, fixed workflow, deterministic program or human decision.
2 min read03 / Design
Where should choice enter the workflow?
Design from a bounded task and evidence, then decide whether one agent, several agents or a fixed workflow fits it.
Practical guide
How to Design an AI Agent Workflow
Turn a real objective into bounded steps, model decisions, tools, checks, approval gates, failure paths and stopping conditions.
2 min readComparison
AI Workflow vs AI Agent: Which Should You Choose?
Choose an appropriate point between fixed workflow, AI-assisted workflow, agentic workflow and higher-autonomy agent.
2 min readComparison
Single-Agent vs Multi-Agent Systems
Compare one capable agent with specialist and coordinator patterns, including handoff cost, duplicated work and error propagation.
2 min read04 / Connect
How does an agent reach data and systems?
Connect tools, APIs, retrieval and MCP while keeping schemas, context and permissions explicit.
Explainer
What Are Tools and Function Calling in AI Agents?
Understand how a model requests a tool, how trusted application code executes it and how observations return to the agent loop.
2 min readExplainer
Memory, Context and State in AI Agents
Distinguish current context, conversation history, working state, persistent memory and authoritative external data.
2 min readComparison
RAG vs AI Agents: What’s the Difference?
Distinguish retrieval-augmented generation from agentic action and understand how retrieval can support an agent workflow.
2 min readExplainer
How Do AI Agents Connect to Tools, APIs and MCP?
Understand APIs, tool definitions, connectors and Model Context Protocol as different layers between an agent and external capabilities.
3 min read05 / Automate
Which actions can proceed, pause or escalate?
Move from deterministic automation toward bounded autonomy, with people retained at consequential decision points.
Explainer
From Automation to Agentic Automation
See what changes when automation can interpret inputs and choose actions—and which exact rules should remain deterministic.
2 min readPractical guide
Human-in-the-Loop AI: When Should an Agent Ask for Approval?
Place human approval and escalation where consequence, uncertainty, sensitive data or external communication require authority.
2 min read06 / Control
How do you contain failure, access and cost?
Plan for unreliable outputs, untrusted inputs, excessive permissions, repeated attempts and resource limits.
Explainer
Why Do AI Agents Fail?
Trace failures across goals, planning, context, tools, permissions, observations, verification, loops and stopping conditions.
2 min readPractical guide
Permissions, Guardrails and Security for AI Agents
Apply least privilege, approval gates, isolation, secret handling and audit trails around model-directed tool use.
2 min readPractical guide
How to Control the Cost of AI Agents
Control repeated model calls, growing context, tool use, retries, multi-agent overhead, verification and latency.
2 min read07 / Scale
What evidence earns production use?
Evaluate outcomes and trajectories, then add monitoring, ownership, rollback and change control before expanding scope.
Practical guide
How Do You Evaluate an AI Agent?
Evaluate agent outcomes, trajectories, tools, intervention, cost, latency, safety and value across representative repeated trials.
2 min readPractical guide
From AI Agent Experiment to Production
Move from prototype to controlled pilot, measured workflow and owned production system with evaluation and rollback.
2 min read08 / Perspectives
Could stopping be a core capability?
Consider why useful autonomy depends on knowing when to continue, ask, escalate or stop.
Perspective
The Best AI Agents May Be the Ones That Know When to Stop
An OpenSkool perspective on appropriate autonomy: when an AI system should act, verify, ask, escalate or stop.
2 min readStart with one bounded action.
Choose a repeated task and write down its goal, evidence, allowed tools, permissions, success check, budget and stopping conditions. Test the smallest useful action before widening the system's authority.
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