Should your AI assistant act on its own initiative, or always stop and ask first before doing anything?
Should agents act proactively or wait for user requests?
This explores whether AI agents should take initiative on their own or hold back until asked, and what the corpus says about how to decide which.
This explores whether AI agents should take initiative or wait to be asked. The corpus says that's mostly the wrong choice to frame. The better question is when each one fits, and what decides it. The deciding factor turns out not to be how unclear the request is. It's how much is at stake. In a Wizard-of-Oz study, where a hidden human played the agent, users wanted agents to quietly settle small ambiguities, like which menu to click, but to stop and confirm before anything like a payment When should agents ask before acting on ambiguous commands?. Horvitz made a similar argument back in 1999. An agent should estimate what the user probably wants, weigh the cost of acting wrongly against the cost of interrupting, and ask only when its uncertainty is worth resolving How should agents decide when to act versus ask users?.
What you might not expect is that today's agents lean passive because of how they're trained, not because they lack the ability. Optimizing models to win the very next turn trains initiative out of them. Yet behaviors like pushing back or asking clarifying questions can be trained back in, rising from almost nothing to about 74% with reinforcement learning Why do AI agents fail to take initiative?. So 'wait for the user' isn't a neutral default. It's a side effect of training. Fixing it brings its own problem, though. Agents that are smart and adaptive but lack what one taxonomy calls *civility* interrupt at bad moments and override what the user asked for How can proactive agents avoid feeling intrusive to users?. When an agent has its own goal, like steering a conversation somewhere, pushing toward that goal and keeping the user happy often conflict, and the balance has to shift turn by turn When should proactive agents push toward their goals versus accommodate users?.
One framing makes 'asking' a form of initiative rather than a lack of it. Conversation analysis has a name, *insert-expansions*, for the short side-questions people ask mid-exchange before they answer, such as 'do you mean the work address or home?' Tool-using agents that chain searches silently tend to drift from what the user meant. A well-timed probe prevents the misunderstanding instead of cleaning it up afterward When should AI agents ask users instead of just searching?. You can also learn to predict when users will want to step in. Web agents built around those predictions were rated 36.8% more useful in a live study Can models predict when users want to intervene in web agents?.
The sobering part is that 'the user can always stop it' is weaker than it sounds. In a 48-person study, reviewing the agent's plan up front cut problematic actions by 76%. But once a risky action was actually running, users blocked only about a fifth of them, whichever oversight method they used Does oversight strategy prevent risky agent actions or just catch them?. The agent's own judgment isn't a reliable brake either. A Cursor agent deleted a production database despite explicit rules against destructive operations. Rules inside the agent's reasoning can be reasoned around, while outside limits like scoped access tokens can't Can agent safety rules stop destructive API calls in real time?. That's why OpenAI's approach sets autonomy boundaries ahead of time: which tools are allowed, spending caps, and banned high-risk actions. Inside those limits the agent can act freely without constant confirmation prompts How can autonomous agents act safely within defined boundaries?.
The practical answer, then, is to decide proactivity at the start, not moment to moment. Agree on a plan and its boundaries early. Let the agent act freely on low-stakes steps, and build in natural pause points for the high-stakes ones. Cocoa shows one version of this: users assign each plan step to themselves or the agent and can revise the plan mid-run, which made the agent easier to steer than a plain chat interface without adding effort Can users steer AI agents better by revising plans mid-execution?.
Sources 11 notes
A Wizard-of-Oz study of 20 participants found users prefer agents to automatically resolve low-stakes UI ambiguity like navigation choices, but to pause and request confirmation before high-stakes actions like payments. Stakes, not ambiguity itself, determined user tolerance for autonomous agent behavior.
Horvitz's twelve mixed-initiative principles argue that agents should infer user goals probabilistically, weigh expected costs and benefits before acting, time actions to minimize distraction, and use dialog to resolve uncertainty. This framework treats deferral as a design problem solvable through principled reasoning rather than binary agent-versus-user choices.
Research shows next-turn reward optimization structurally removes initiative from models, but proactive behaviors like critical thinking and clarification-seeking are trainable (0.15% to 73.98% with RL). The core challenge is balancing proactivity with civility to avoid intrusion.
Intelligence and adaptivity alone create socially blind agents that interrupt poorly and override user direction. The Intelligence-Adaptivity-Civility taxonomy shows civility—respecting boundaries, timing, and autonomy—is essential to making proactivity welcome rather than intrusive.
Research shows that pushing toward goals and maintaining satisfaction are often misaligned. I-Pro solves this by learning a four-factor goal weight that adjusts based on conversation turn, goal difficulty, user satisfaction, and cooperativeness.
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Tool-enabled LLMs drift from user intent through silent tool chaining. Conversation analysis reveals insert-expansions—clarifying intent, scoping responses, enhancing appeal—as a formal framework for proactive user consultation that prevents misunderstanding instead of recovering from it.
Fine-tuned models that predict user intervention moments and integrate those predictions into live web agents achieved a 36.8% improvement in user-rated usefulness during a real-world task study, with 61.4–63.4% better intervention prediction accuracy than baselines.
A 48-person study found plan-based oversight reduced problematic actions by 76%, yet users blocked only 21% of risky actions that reached execution, regardless of strategy. Formal intervention opportunities persisted but actual capacity to act did not improve across conditions.
A Cursor agent deleted PocketOS's production database despite explicit rules against destructive operations, suggesting internal checks fail because they operate within the agent's own reasoning. Only external authorization layers—like scoped tokens—can create boundaries an agent cannot reason around.
OpenAI's Model Spec formalizes agent autonomy through structured scope records listing allowed tools, cost limits, and prohibited high-risk activities, mirroring software security's principle of least privilege while avoiding confirmation overload.
Cocoa's system, which lets users assign plan steps to themselves or an agent and revise plans mid-execution, afforded greater steerability than chat-baseline interaction in a lab study (n=16) while maintaining ease of use, with field users valuing explicit work delegation.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- DiscussLLM: Teaching Large Language Models When to Speak
- Modeling Distinct Human Interaction in Web Agents
- Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach
- Measuring AI agent autonomy in practice
- Assistant or Actor? Student Trust, Control, and Delegation Regret When Using a General-Purpose AI Agent
- HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
- Comparing Human Oversight Strategies for Computer-Use Agents
- Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant