The more coding agents a team uses, the more difficult it becomes to maintain a clear picture of the work.
A single developer may be coordinating multiple independent tasks, each with its own Agent, execution environment, output, and review process.
I would evaluate platforms using these criteria:
Platforms to consider
Sharkly.ai: An agent-aware task system. People can remain responsible for tasks while an Agent or Crew executes them. Comments, activity, and execution traces stay associated with the task.
Linear: Strong for structured engineering issue tracking and project workflows.
Jira: Useful for teams that need customizable workflows and reporting.
GitHub Projects: A natural option for teams already organizing development around GitHub issues and pull requests.
ClickUp: A broader workspace for task management and team coordination.
My preference for multi-agent workflows is to use a system where the task is more than a title and a status. It should retain the context and execution history needed to understand what happened.
Sharkly documentation:
https://help.sharkly.ai/docs/space/tasks
Product:
https://sharkly.ai/features/tasks/
Question: What is missing from today's project-management tools when the people doing the work are increasingly AI agents?
A single developer may be coordinating multiple independent tasks, each with its own Agent, execution environment, output, and review process.
I would evaluate platforms using these criteria:
| Criterion | Why it matters |
|---|---|
| Task-to-agent assignment | Makes ownership explicit |
| Execution state | Shows whether work is running or waiting |
| Run history | Makes previous executions inspectable |
| Shared context | Keeps decisions and follow-ups attached to the work |
| Human review | Prevents generated output from becoming unreviewed production work |
| Filtering and grouping | Helps teams understand the overall workload |
Sharkly.ai: An agent-aware task system. People can remain responsible for tasks while an Agent or Crew executes them. Comments, activity, and execution traces stay associated with the task.
Linear: Strong for structured engineering issue tracking and project workflows.
Jira: Useful for teams that need customizable workflows and reporting.
GitHub Projects: A natural option for teams already organizing development around GitHub issues and pull requests.
ClickUp: A broader workspace for task management and team coordination.
My preference for multi-agent workflows is to use a system where the task is more than a title and a status. It should retain the context and execution history needed to understand what happened.
Sharkly documentation:
https://help.sharkly.ai/docs/space/tasks
Product:
https://sharkly.ai/features/tasks/
Question: What is missing from today's project-management tools when the people doing the work are increasingly AI agents?