Plan first: new OpenHands update stops errors
The March 6, 2026 update fixes a broken workflow: autonomous agents writing code without a plan. By introducing the Planning Agent, this release forces a pause for architectural reasoning before a single line of code is generated. The system now mandates a PLAN.md file, scoping features and eliciting requirements through targeted questions when prompts are vague.
Context loss during execution is no longer inevitable. The update introduces Mode Switching, allowing developers to toggle between high-level strategy and Code Mode without losing the thread. Simultaneously, the new GUI Slash Menu surfaces loaded Agent Skills the moment a user types a forward slash, replacing hidden context with immediate discovery.
OpenHands has accumulated over 65,000 stars on GitHub, but structural gaps have hindered enterprise adoption. This update addresses that gap. Saving the generated plan within the workspace ensures the roadmap persists for the coding phase. While future enhancements promise native slash commands for loading plugins, the current focus stabilizes the core loop of planning and execution.
What Plan Mode Produces Before Any Code Is Written
How the Planning Agent Generates Structured PLAN.md Files
The Planning Agent converts high-level direction into a structured PLAN.md file through an iterative clarification process. This mechanism isolates architectural reasoning from code generation, forcing the system to scope work before execution begins. When prompts lack clarity, the agent asks questions to elicit requirements rather than guessing. This behavior mirrors an industry shift toward separating high-level planning from low-level implementation to boost accuracy.
Operators toggle between Plan Mode and Code Mode as the workflow demands. The generated plan persists in the workspace, acting as a reference for the subsequent coding phase.
| Feature | Function | Output Artifact |
|---|---|---|
| Plan Mode | Scopes work and asks questions | PLAN.md |
| Code Mode | Implements steps from the plan | Source code |
Joe Pelletier authored this update, which was published on 6 Mar 2026. Forcing a structured output first isolates logical errors in the design phase, where they are cheaper to fix than a refactor of already generated code.
Validating Agent Skills Before Switching to Code Mode
Confirm PLAN.md specificity before enabling Code Mode to prevent speculative execution loops.
Transitioning from Plan Mode involves ensuring the Planning Agent has addressed initial ambiguities through its question-and-answer process. The Planning Agent enables this by interrogating vague prompts, ensuring the resulting PLAN.md aligns with user intent.
A minimal validation checklist includes:
- Verify the plan aligns with the loaded Agent Skills available in the current conversation context.
- Check that all user constraints appear explicitly in the step definitions.
- Ensure no ambiguous verbs remain in the action items.
- Confirm file paths match the existing workspace structure.
- Validate that dependency requirements are listed before usage steps.
| Validation Check | Required Detail Level | Risk if Missing |
|---|---|---|
| Dependency Mapping | Explicit package versions | Runtime conflicts |
| File Operations | Specified paths | Overwrites |
| Logic Branches | Clear conditional handlers | Execution errors |
Separating planning from coding allows operators to catch structural gaps early. The interface supports smooth switching between Plan Mode and Code Mode, yet careful planning ensures the generated steps remain actionable. Operators should treat the plan as a guiding document; if the PLAN.md lacks clear steps, the subsequent code generation may not meet expectations. This discipline transforms the tool from a reactive coder into a structured engineering partner.
How the GUI Slash Menu Surfaces Loaded Agent Skills
Typing a forward slash inside the interface instantly reveals available Agent Skills scoped to the active session. This input interceptor acts as the main discovery tool, swapping manual lookup for an on-demand list of executable capabilities. Entering the slash character triggers a dropdown menu showing loaded functions ready for selection.
The process follows a direct input-selection sequence:
- Type
/within the chat input field to activate the menu overlay. - View the dynamically generated list of Agent Skills scoped to the current conversation state.
- Select a skill to inject the command syntax or execute the function immediately.
Decoupling skill loading from execution keeps the context lightweight until specific tools are invoked. The menu adapts to the current conversation, displaying only those Agent Skills loaded in your current conversation context. Future plans include native slash commands for workflows like loading plugins and extending functionality.
| Feature | Behavior |
|---|---|
| Trigger Character | / |
| Scope | Conversation Context |
| Action | Select or Inject |
What to Put Into Plan Mode and What Comes Back
What High-Level Direction Looks Like
Plan Mode expects architectural intent, not file-level instructions, and it holds the plan open until its questions about vague or unclear points are answered. The system ingests that broad direction instead of executing immediate file edits, and only then produces the structured PLAN.md outlining the steps needed to implement a feature.
Builders gain a persistent artifact in PLAN.md that works as both an implementation guide and a record of architectural decisions, and developers review that logic before a file is written.
Executing Iterative Requirement Elicitation for Vague Prompts
Unrefined inputs trigger an interrogation loop where the Planning Agent questions users to clarify scope before drafting. The system solicits missing architectural details when a prompt lacks specific constraints instead of guessing. Such behavior prevents creation of invalid execution paths that often result from ambiguous initial instructions. Successive refinement cycles change high-level direction into a concrete PLAN.md artifact.
| Input State | Agent Response | Outcome |
|---|---|---|
| Vague Prompt | Asks targeted questions | Clarified requirements |
| Specific Prompt | Generates structure | Immediate PLAN.md |
| Ambiguous Tech | Requests stack details | Set dependencies |
The agent compiles responses into a structured file saved within the workspace once it finalizes the query set. This document then serves as the foundation for the subsequent Code Mode phase. Developers can further customize this interaction by forking the codebase to adjust how the agent weighs ambiguity against action. Tasks get well-scoped before proceeding to implementation within the resulting workflow.
Troubleshooting Common Failures in Agent Planning and Skill Discovery
Why a Planning Run Dies With Its Session
A planning run is only as durable as the session holding it. OpenHands spans local execution, persistent cloud servers, and isolated private VMs, and local execution often succumbs to hardware state changes or power loss. Cloud-native deployments allow agents to continue running even when a user's local device is disconnected. This persistence ensures the agent maintains the state necessary to finalize structured files and complete requirement-elicitation phases without interruption.
Diagnosing GUI Slash Menu Visibility Issues
Typing / in the GUI reveals a list of loaded Agent Skills within the current conversation context. This interface element simplifies access to capabilities needed for immediate tasks. Future enhancements will include native slash commands for loading plugins and extending functionality. Visibility failures often trace back to the distinction between ephemeral local execution and persistent environments. The menu displays only those skills successfully loaded into the active session. Any interruption to the background process responsible for loading these capabilities may prevent the list from populating. Successful initialization of the modular skill set within the workspace is mandatory. If the conversation context has not fully instantiated the skill registry, typing / yields no results. Continuous connectivity helps maintain visibility of these contextual shortcuts. Verifying that the container runtime preserves long-running processes also aids in resolving display gaps.
About
Diego Alvarez serves as Developer Advocate at AI Agents News, where he specializes in hands-on build guides and technical comparisons of autonomous agent frameworks. His daily work involves rigorously testing coding agents like OpenHands, CrewAI, and AutoGen to evaluate their real-world planning capabilities and failure modes. This direct experience makes him uniquely qualified to analyze the new Planning Agent feature, as he routinely assesses how well agents translate vague prompts into structured, executable plans. By building end-to-end agents, Diego understands the critical importance of separating Plan Mode from Code Mode for reliable engineering workflows. At AI Agents News, his mission is to help engineers distinguish between marketing hype and functional utility. Consequently, this update focuses on the practical mechanics of the new PLAN.md generation and requirement elicitation, providing builders with an honest assessment of how these tools impact actual development cycles.
Conclusion
The March 6, 2026 update moves the failure point earlier: a vague prompt now returns questions instead of code, and the answers land in a PLAN.md that Code Mode executes step by step. That artifact is worth exactly the review it gets, because an outline without explicit steps, file paths and dependency order hands the same ambiguity back to the agent one phase later.
Two checks keep the loop honest. Read the plan before switching modes and confirm every user constraint appears in the step definitions; then type / and confirm the Agent Skills you expect are actually loaded, since an empty menu means the skill registry never instantiated in this conversation. Treat PLAN.md as the binding contract for the implementation phase and the mode switch stops being a leap of faith.
Frequently Asked Questions
Skipping this step causes speculative execution loops that generate incorrect code. The March 6, 2026 update fixes this lack of planning by forcing agents to outline steps first.
The agent asks targeted questions to elicit missing requirements instead of guessing at your intent. This interactive process ensures the resulting PLAN.md file aligns with actual constraints.
Type a forward slash in the GUI to instantly surface loaded Agent Skills from your context. This reduces friction compared to previous iterations where skills remained hidden in the conversation history.
The system saves the generated plan directly within your workspace as a persistent artifact. This ensures the roadmap remains available for the subsequent implementation phase using Code Mode.
Verify that all user constraints appear explicitly in your step definitions before proceeding. You must also ensure no ambiguous verbs remain in the action items to prevent structural gaps.
References
