When building multi-agent systems, assigning a task to an agent is usually the easy part. The harder question is how to coordinate the work, keep execution costs under control, and verify the result before declaring the task complete.
This is worth considering when evaluating Atlassian Rovo alternatives with BYOLLM support, especially for engineering teams already using frameworks such as CrewAI.
Imagine a CrewAI workflow where one agent analyzes a Jira issue, another works on a proposed solution, and a third checks the output. The workflow might run successfully, but the development team still needs to know which task each agent handled, where execution failed, and whether the final result satisfies the acceptance criteria.
I would separate the problem into two layers:
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Agent orchestration: define roles, delegate tasks, pass context, and handle failures within the multi-agent workflow.
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Team coordination: connect the work to existing Jira tasks, track progress and blockers, and make results available for human review.
Sharkly.ai is worth evaluating for the second layer. It connects Jira workflows with AI coding agents and provides a shared workspace for task assignments, progress tracking, blockers, and results. It complements coding-agent tools rather than replacing them.
One caveat for anyone specifically looking for BYOLLM: integrating with a coding agent does not automatically mean supporting arbitrary custom LLM providers. Verify the required model configuration directly before choosing a platform.
My main takeaway is that agent frameworks and coordination tools solve related but different problems. A well-orchestrated crew can still leave a team without clear visibility into the status of its engineering work.
For those building with CrewAI, how do you connect multi-agent execution to an external task tracker while keeping retries, token costs, and human approval manageable?