What is the main takeaway regarding Building Your First Agent Loop: Plan, Execute, Evaluate, Iterate?

An agent loop is an AI that can plan, execute, check its own work, and try again. Here is the basic architecture and when to use it.

3 Things I Wish I Knew Before Building My First Agent Loop

A single AI call is a transaction. You send a prompt, you get a response, you’re done. That’s useful for simple tasks, but it can’t handle complex, multi-step work that requires judgment and adaptation.

An agent loop is different. It’s an AI that can plan a sequence of actions, execute them, evaluate the results, and iterate until the task is complete - or until it needs human input.

The Four Steps of an Agent Loop

1. Plan

The agent receives a high-level goal and breaks it down into a sequence of concrete steps. “Audit this website’s SEO” becomes: (1) crawl the site, (2) check keyword rankings, (3) check technical health, (4) check GBP, (5) compile findings, (6) generate recommendations.

2. Execute

The agent works through the steps, using tools (web crawlers, search APIs, database queries) to gather data and produce outputs.

3. Evaluate

After each step, the agent checks whether the output meets the required quality standard. Did the crawl return data? Are the keyword rankings complete? If a step fails or produces incomplete output, the agent flags it.

4. Iterate

If a step fails, the agent tries an alternative approach. If the task requires human input (a decision, an approval, a piece of information only the human has), the agent pauses and surfaces the question.

When to Use an Agent Loop

Use agent loops for tasks that:

Require multiple steps in sequence
Have variable paths depending on what is found
Need to recover gracefully from partial failures
Require a final human review before output is used

Don’t use agent loops for simple, single-step tasks. A loop adds overhead. For “summarize this document,” a single call is faster and cheaper.

Related reading:

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