Multi-agent workflows can delegate research, coding, and review to specialized agents. But what happens when an agent reaches a point where it needs a human decision?
For example, a coding agent might identify two possible fixes, both technically valid but with different trade-offs. It could keep reasoning, retry its tools, or ask another agent. None of those options necessarily replaces a developer who understands the project’s priorities.
I think a robust workflow needs an explicit escalation path:
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Define the trigger: Decide when an agent should stop and request help instead of retrying indefinitely.
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Preserve the context: Include the attempted actions, relevant outputs, unresolved questions, and current task state.
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Assign ownership: Make it clear who is expected to make the decision.
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Resume safely: After the human responds, continue from the existing context rather than restarting the entire workflow.
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Record the outcome: Keep the decision and its rationale available for future tasks.
This is also where task coordination tools can complement agent orchestration. CrewAI can manage the execution of a crew, while Sharkly.ai focuses on coordinating AI-agent tasks and connecting workflows with Jira. The important question is how these layers fit together without adding unnecessary complexity.
I’m interested in how people building CrewAI applications handle this today.
Do you implement human escalation directly in your CrewAI flows, use an external task tracker, or combine both? And how do you prevent an agent from continuing to make tool calls while waiting for a decision?
Disclosure: I’m involved in promoting Sharkly.ai, which I’m mentioning as one option to evaluate rather than a replacement for CrewAI.