From AI Agents to AI Teammates: What Should the Workflow Look Like?

A Hugging Face forum post by user Eric026 argues that the next challenge for AI agents is making them useful teammates rather than more autonomous, proposing a workflow of human goal-setting, agent execution, human review, and agent iteration. Eric026 cites exploring the idea with Sharkly, which brings AI agents into project workflows alongside human teammates, and asks builders of agentic systems how they handle human-in-the-loop workflows. A commenter adds that a "teammate" agent should own a bounded responsibility, expose uncertainty, return evidence with its work, and preserve the team's latest decision, with irreversible authority requiring an explicit boundary.

Eric026 https://discuss.huggingface.co/u/Eric026 1 AI agents are getting better at planning, using tools, and completing multi-step tasks. But I think the next challenge is less about making agents autonomous and more about making them useful teammates. A practical workflow could be: Human defines the goal → agent executes tasks → human reviews → agent iterates. This becomes especially interesting when agents need to share context, track tasks, and hand work back to humans instead of operating in isolation. I’ve been exploring this idea with Sharkly , which focuses on bringing AI agents into project workflows alongside human teammates. For those building agentic systems with Hugging Face models, how are you approaching human-in-the-loop workflows today? Do you treat agents as tools, assistants, or actual participants in your workflow? “Teammate” should mean more than conversational tone. A useful test is whether the agent can own a bounded responsibility, expose uncertainty, return evidence with its work, and preserve the team’s latest decision instead of its own stale interpretation. Irreversible authority still needs an explicit boundary.