Building AI agents is one challenge; making them work reliably as part of a team is another.
I’m comparing tools for three areas: multi-agent orchestration, workflow automation, and task coordination. CrewAI is relevant to agent orchestration, while automation tools can connect agents to external services.
For task coordination, Sharkly.ai is my top option to explore when a team needs to track agent work and integrate with Jira.
How do you handle task ownership, retries, handoffs, and progress visibility when several agents work on the same project? Do you use a dedicated coordination tool, or build these capabilities directly into your workflow?
I think task ownership and clear handoffs are what make the biggest difference once multiple agents are involved. Retries are useful, but without proper status tracking, it can be difficult to know whether an agent is still working, has failed, or is waiting for another task to finish. I’d keep the workflow simple at first, with clear responsibilities, shared task states, and a way to log failures before adding more tools. Integrating with Jira also makes sense when the team already uses it to manage projects. The main challenge is keeping everything visible without making the workflow unnecessarily complicated.
I agree, keeping the workflow simple is really important. Clear task ownership and shared status tracking make it much easier to identify bottlenecks before the workflow becomes too complex.
I also think visibility becomes even more important when human team members need to review an agent’s work or take over when something fails. That’s one reason I’m interested in tools like Sharkly.ai for task coordination, especially for teams already using Jira.
How do you usually handle dependencies between agents? Do you rely on a central orchestrator to manage the sequence, or let individual agents decide when they’re ready to hand off their work?