How should users and AI agents share control of a task?
Let users co-plan and assign steps, interleave planning with doing, and hand control back on risky steps. Early studies show this steers agents without making them hard to use.
Early signal, tested in lab or early LLM studies
Treat it as a hypothesis.
Two builder-run system studies with small samples and simulated users.
2 studies · 2 with LLMs · 2025–2026 · How we rate
Make the plan editable, let steps be assigned to the user or agent, and support easy handover.
- Show the plan before execution and let users edit it.From the study
- Let users assign individual steps to themselves or the agent.From the study
- Allow the plan to change after partial execution.From the study
- Ask for help sparingly; it was needed in a minority of tasks.From the study
What they found
Feng et al. (2026)system evaluation
After a formative study with 9 researchers, the authors built Cocoa, a notebook-style agent where plan steps can be assigned to the user or the agent and planning and execution interleave. A lab study (16 people) found it enabled steering without sacrificing ease of use; a week-long field deployment (7 people) showed it used on real research tasks. Builder-run evaluation.
“Cocoa enabled steerability without sacrificing ease-of-use”
Feng et al., 2026 · Abstract
Mozannar et al. (2025)system evaluation
A GPT-4o web agent with co-planning, action approval for irreversible steps, and handover to the user. On 162 GAIA validation tasks, a simulated user with side information raised task completion from 30.3% (autonomous) to 51.9%, while the agent asked for help in only 10% of tasks. A qualitative study with 12 users explored the features; this is a system paper by its builders, not an independent test.
“Magentic-UI implements an action guard system to ensure irreversible or potentially harmful agent actions are reviewed by the human user.”
Mozannar et al., 2025 · Figure 8 caption
Trade-offs and when it doesn't apply
- Both are builder-run studies.
- Small samples (16 lab, 7 field, 12 users).
- Simulated users drove the Magentic-UI gains.
What it looks like
How to check it in your product
Track how often users edit plans or take over, and whether those tasks succeed more often.
Open questions
- Which steps should default to the user?
- How much plan detail helps before it becomes noise?
- Do independent evaluations replicate these benefits?
FAQ
Do humans in the loop improve agent results?
In Magentic-UI's evaluation, a simulated user raised task completion from 30.3% to 51.9% on 162 tasks.
Does co-planning make agents harder to use?
Cocoa's lab study (16 people) found it enabled steering without sacrificing ease of use.
Sources
- Cocoa: Co-Planning and Co-Execution with AI AgentsK. J. Kevin Feng, Kevin Pu, Matt Latzke, et al. (2026). CHI 2026 (arXiv:2412.10999v4).
- Magentic-UI: Towards Human-in-the-loop Agentic SystemsHussein Mozannar, Gagan Bansal, Cheng Tan, Adam Fourney, et al. (2025). arXiv:2507.22358 (Microsoft Research, 2025).