Warp: Interactive Agent Conversations with Clarifying Questions and Follow-Up Suggestions
WarpWarp introduced a significant upgrade to its agent interaction model, enabling agents to ask users clarifying questions mid-task when additional context is needed. Warp agents now also proactively suggest follow-up actions upon completing a task, creating a more guided and conversational development workflow. These two changes together transform Warp's agent from a one-shot executor into a true interactive collaborator capable of iterative back-and-forth dialogue.
Key Takeaways
- Agents can now ask clarifying questions mid-task, allowing Warp to steer agents before they go down the wrong path based on underspecified instructions.
- Follow-up suggestions appear when a task completes, eliminating the blank-slate moment at the end of a session and keeping the development loop moving.
- The two features work together to close the agent interaction loop: clarifying questions before execution, follow-ups after completion.
- Ambiguity in complex tasks is addressed proactively, rather than requiring developers to undo incorrect agent actions after the fact.
- The agent interaction model shifts from command-response to true dialogue, making Warp's agent behave more like a collaborative pair programmer.
- This positions Warp's agent interaction model closer to Cursor's Plan Mode, which pioneered interactive question-based clarification before agent execution.
Sources & Mentions
5 external resources covering this update
A More Conversational Agent Experience
Warp has fundamentally changed how developers interact with its AI agents by introducing two complementary features that make agent sessions feel more like a collaborative dialogue than a series of isolated commands.
Agents Can Now Ask Clarifying Questions
Previously, Warp's agents would proceed with whatever context was available — sometimes making incorrect assumptions when a task was ambiguous. With this release, agents can now pause mid-task to ask users clarifying questions when they need more information to proceed correctly.
This change addresses one of the most common friction points in agentic workflows: the agent going down the wrong path because it lacked a critical piece of context. Rather than having to undo the agent's work after the fact, users are prompted at the right moment and can steer the agent before it diverges from their intent.
The feature is particularly valuable for complex tasks where initial instructions may be underspecified — for example, when asking an agent to refactor a module without clarifying which patterns to apply, or deploying a service without specifying the target environment.
Suggested Follow-Ups When a Task Completes
Complementing the clarifying questions feature, Warp agents now suggest relevant follow-up actions once they finish a task. After completing a code change or running a workflow, the agent surfaces contextual next steps — such as writing tests for the newly created code, reviewing affected files, or running a deployment.
This eliminates the blank-slate moment at the end of a session where developers have to think of what to do next, keeping the development loop moving with minimal friction.
Impact on the Agent Interaction Loop
Together, these two features close the loop on agent interactivity:
- Before task completion: the agent asks clarifying questions to ensure it understands the goal
- After task completion: the agent suggests what should happen next
This makes Warp's agent significantly more productive for complex, multi-step workflows where context and sequencing matter — without requiring the developer to micromanage every step.