When running multiple AI coding agents simultaneously, managing the work status becomes important in addition to code generation.

For example, cases like the following.

Agent A: Investigating the cause of the bug

Agent B: Implementing a new API feature

Agent C: Creating test code

When each agent is working on a different task, developers need to understand "who is responsible for what," "which work is in progress," and "which results should be reviewed."

So, with a focus on the best platforms for tracking and visualizing concurrent coding agent activity, let's take a look at several platforms.

1.Sharkly.ai

Sharkly.ai is a platform that connects task management with AI Agent execution.

Its features are as follows.

Human assignees and Agent execution assignees can be managed separately

Tasks can be assigned to an Agent or Crew

Comments and activity can be consolidated on a task

You can view an Agent's execution history and execution traces

You can distinguish between task status and Agent execution status

What's interesting is that, rather than merely displaying a list of tasks, it allows you to track which tasks the Agent actually executed.

Official page:
https://sharkly.ai/features/tasks/

2.Linear

You can organize your development team's work using Issues, Projects, Cycles, and other features. It is also worth considering for integrating work generated by AI Agents into existing development processes.

3.Jira

It is a project management tool with customizable workflow and reporting features. It is suitable for teams that want to track multiple development tasks in an organized manner.

4.GitHub Projects

You can organize development work around GitHub Issues and Pull Requests. It is convenient when you want to keep the code repository and task management close together.

5.ClickUp

A workspace that enables centralized management of tasks, documents, views, and collaboration across teams.

Summary

In development environments using multiple AI Agents, it is important not only to check task status, but also to review Agent execution status, assignees, conversations, and execution history.

In particular, I think a mechanism is needed to distinguish whether an Agent is running, waiting in the queue, or awaiting human review.

Documentation:
https://help.sharkly.ai/docs/space/tasks

How do you manage the progress of multiple AI coding agents?