AI workflow guide showing sequential task completion from Agent A to Agent B in Claude interface, illustrating efficient mult

Editorial illustration for Guide: Direct Agent A to complete tasks before prompting Agent B in Claude

Guide: Direct Agent A to complete tasks before prompting...

Guide: Direct Agent A to complete tasks before prompting Agent B in Claude

By AI Daily Post Edited by Brian Petersen, Editor-in-Chief

June 23, 2026 • 3 min read

Why does this matter? Because the way we’ve been handling coding agents so far feels more like a relay race than a marathon. Traditionally, you spin up a new agent, watch it implement a piece of code, verify the output, then move on to the next agent.

It works, but you stay glued to every step. Loops change that rhythm. While the tech is impressive, the real gain comes from letting an agent run more autonomously—plan, execute, and verify without your constant oversight.

Here’s the thing: when you set up a loop, Agent A finishes its task before you even think about prompting Agent B. That shift frees up your time, letting you focus on higher‑level decisions instead of micromanaging each line of code. The article walks through why loops let you do more work, and it lays out concrete techniques to make those loops effective.

Expect a step‑by‑step look at planning with Agent A, letting it run to completion, and only then bringing Agent B into the conversation. It’s a modest tweak, but one that could reshape how we collaborate with Claude’s coding agents.

Agent A will then start working on a task, and you tell it to only come back to you once it's finished that task. Once you've finished instructing Agent A, you can start doing the same for Agent B, setting a goal and having it work in a loop. This time, Agent A doesn't interrupt you for more input because it has the self-verification loop and doesn't need your input in the same way anymore.

Thus, you can continue setting tasks on agents C, D, E, and so on, until agent A finishes its work. The conclusion here is that scenario 2 simply allows you to spin up more agents and complete more tasks, which is, of course, incredibly valuable because it allows you to do more work at once. How to work in loops Now, the big question, of course, is how do you actually work in loops?

There are a lot of different ways to do it, but I'll cover the simplest technique that you can start implementing right away. This technique is to use the /goal command with either Claude Code or Codex.

How to Create Powerful Loops in Claude Code - Towards Data Science

Why this matters

We can finally let Claude’s agents run longer loops without constant human prompts. No magic here. By telling Agent A to finish a task before it returns, and then setting up Agent B in the same way, developers may reduce the back‑and‑forth that previously slowed coding workflows.

Can we trust loops? This shift from “spin up, verify, repeat” to autonomous cycles promises more end‑to‑end completion. Yet, the article offers no data on error rates when agents operate unattended; it’s unclear whether hidden bugs will surface only after a loop has run its course.

Founders might see faster prototyping, but they will still need safeguards to catch missteps before they compound. Researchers can explore how loop length affects performance, though the guide stops short of defining optimal boundaries. In practice, the approach could free us to focus on higher‑level design, but it also raises questions about oversight and accountability.

Ultimately, the technique expands Claude’s toolbox, but its real‑world impact will depend on how rigorously teams monitor the autonomous phases.