Planning Mode in Visual Studio 2022: Copilot Orchestrates Multi‑Step Plans (Public Preview)

Microsoft’s Visual Studio is getting a fundamentally different way to work with Copilot: Planning Mode, a Copilot-powered planning workflow that builds structured, multi-step plans, executes them step by step inside the IDE, and continuously updates progress in a visible markdown plan file — now available in public preview for Visual Studio 2022 version 17.14.

Background​

Visual Studio and GitHub Copilot have steadily moved from simple line-by-line suggestions toward agentic, multi-step workflows that can reason across files, call tools, and iterate on tasks. Planning Mode arrives as the next iteration of that evolution: instead of answering a single prompt with a short text or a code completion, Copilot now produces a living plan — a checklist and execution log — that it follows and revises as it works. This is part of a broader push across the Copilot ecosystem to support Agent Mode and the Model Context Protocol (MCP), which let AI-driven agents access structured project context and external tools for deeper, auditable automation.

Why Planning Mode matters now​

Short prompts and single-turn completions are fast but brittle for complex work. For tasks that require research, cross-file edits, testing, and repeated fixes, a line-by-line approach fragments the reasoning process. Planning Mode offers a structured, transparent workflow: the model writes a plan, shows progress, and updates the same plan as it performs each step. That makes Copilot’s behavior more predictable, more auditable, and easier to review — attributes enterprises value when introducing automated changes to codebases. Microsoft rolled this capability into Visual Studio 2022 (v17.14) as a public preview, enabling developers to try the experience inside the familiar Visual Studio UI.

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How Planning Mode works​

Planning Mode leverages Copilot’s agentic capabilities to create and manage multi-step tasks inside Visual Studio. The feature follows a short, repeatable workflow that blends planning and execution.

The plan file lifecycle​

When Copilot determines a multi-step task is required, it generates a markdown plan file that is saved temporarily under the Windows temporary folder at:

• %TEMP%\VisualStudio\copilot-vs\

That plan contains:

• A short task description and goals
• Research and discovery steps
• A checklist of implementation tasks (with checkboxes)
• Progress markers and execution notes written as the agent runs each step

The file is live: Copilot updates the plan as it executes actions, and developers see the plan evolve in real time. If you want the plan to persist beyond the temp folder, the file can be added to your repository so progress and history are stored in source control. Microsoft explicitly documents these behaviors and the current temporary storage location as part of the preview UX.

Transparent tool calls and iterative execution​

Planning Mode is built on transparent "tool calls" — scoped operations Copilot performs while reasoning through the plan. Instead of hiding internal decisions, the plan exposes what Copilot is about to do and what it already completed. This closed-loop behavior lets the agent:

• Analyze code and tests
• Run compilations or unit tests
• Make edits across multiple files
• Re-run build and test cycles when fixes are needed
• Record outcomes and adjust remaining tasks

That iterative loop — plan → act → check → revise — borrows from hierarchical planning approaches in AI research and helps reduce surprising or opaque edits. Microsoft designed the flow so simple prompts still get immediate quick answers while complex prompts switch into the planning workflow automatically.

When edits and live plan edits collide​

Because the plan file is live and editable, there are a few practical caveats: editing the plan while Copilot is mid-execution may not take effect immediately. Microsoft’s preview guidance recommends stopping the running response before making edits to the plan or prompt; they’re exploring smoother mid-response editing but it isn’t guaranteed in the initial rollout. This behavior preserves deterministic execution during active steps but means developer edits need coordination with the running agent.

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Developer workflow: enabling and using Planning Mode​

Planning Mode is in public preview and rolling out gradually. Developers can enable it inside Visual Studio 2022 (17.14) via the options dialog:

• Tools → Options → Copilot → Enable Planning

Once enabled, multi-step prompts will cause Copilot to create the plan file and begin the multi-step workflow. A typical developer workflow looks like this:

• Formulate a high-level goal in Copilot Chat (for example: "Refactor the order-processing flow to add retry logic and add tests").
• Copilot decides this is a multi-step task and switches to Planning Mode.
• A plan.md file is generated in %TEMP%\VisualStudio\copilot-vs\ and is displayed in the IDE.
• Copilot executes the first research/analysis step (for example, locate the order-processing code and existing tests), updates the plan, and reports progress.
• If edits are needed, Copilot creates a working branch, performs multi-file edits, runs builds/tests, and iterates until goals are met or human intervention is required.
• Developers review the changes, run CI pipelines, and merge the working branch when satisfied; optionally check the plan into source control for auditability.

This pattern shifts Copilot from a suggestion engine to a task driver that can take responsibility for sequences of actions while giving developers full visibility and final control.

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Measured improvements and vendor-reported results — interpret with care​

Microsoft reports early empirical gains from planning-driven workflows. In SWE-bench trials highlighted in the Visual Studio announcement, models like GPT-5 and Claude Sonnet 4 achieved around 15% higher success and 20% more tasks completed when using Planning Mode compared with non-planning runs. Those numbers are promising as an early signal that structured planning helps models handle multi-step engineering tasks more reliably.
However, treat these figures as vendor-provided, context-dependent measurements. Third-party and independent testing historically finds that vendor benchmarking can be influenced by task selection, dataset composition, and configuration choices. Practical reliability on your repositories will vary based on codebase complexity, test coverage, the models selected for Copilot, and organizational governance. It’s best to run representative pilot tests on your own repositories and guard claims with CI and test gates rather than assuming identical gains in every environment.

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Strengths: what Planning Mode brings to teams​

• Predictability and transparency: The plan file shows every intended step, reducing surprise edits and giving reviewers an audit trail of the agent’s reasoning.
• Better for complex tasks: Multi-file refactors, migrations, and feature work that require research and validation are much easier to coordinate when the agent uses a structured checklist.
• Iterative closed-loop execution: Copilot’s ability to act, validate (build/tests), and adjust mimics human workflows and reduces one-shot failures.
• Easier integration with dev workflows: The plan can be committed to the repo and used as a living artifact for code reviews, postmortems, and documentation.

These strengths make Planning Mode attractive for engineering teams that already have mature CI/CD practices and strong test coverage, since the agent can rely on tests as a guardrail while iterating.

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Risks and limitations to watch closely​

• Vendor-benchmarks vs. real-world variance: The SWE-bench numbers are a useful signal but not a substitute for sandboxing the feature against your own code and tests. Validate improvements with controlled pilot experiments.
• Temporary storage and data governance: Plans are initially written to %TEMP%\VisualStudio\copilot-vs\, which may raise data retention and governance questions for organizations with strict policies about local temp storage. If you need permanent audit trails, commit plans to source control after review.
• Secrets and external actions: Agentic edits that call tools, deploy resources, or access external systems increase the attack surface. Ensure least-privilege credentials, service principals, and approvals are enforced for any agent-driven operations.
• Mid-execution plan edits: Editing the plan while the response is running may not immediately affect the running agent. Developers must coordinate edits with active runs, which can be awkward for rapid iteration.
• Model-dependent behavior and governance: Different models behave differently (e.g., OpenAI, Anthropic, Google models), and multi-model arrangements complicate policy and compliance choices. Enterprises should define which models are authorized for which tasks and how outputs are verified.

These risks are not fatal, but they require deliberate governance and operational controls before you let agents perform broad changes across important repositories.

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Technical and QA best practices for safe adoption​

To adopt Planning Mode responsibly across projects, follow a pragmatic set of checks and controls:

• Upgrade to Visual Studio 2022 v17.14 and enable Planning Mode for pilot users.
• Run pilots on representative repositories with good unit and integration test coverage to measure real-world impact.
• Require the agent to create working branches for any non-trivial edits so code reviews remain the gate.
• Enforce CI gates: all agent-created branches must pass automated tests, SAST scans, and other security checks before merging.
• Use least-privilege service principals for any deployments or tool access invoked during agent runs. Log all such actions.
• Treat the plan file as an auditable artifact: commit it to the repo only after review if you require long-term traceability.
• Define a model policy (which models are allowed, BYOM rules) and tenant-level controls for model routing if your organization permits multiple model providers.
• Maintain human-in-the-loop checkpoints for sensitive changes — agentic automation should accelerate engineers, not bypass them.

These steps let teams capture the productivity gains while maintaining auditability and security.

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Enterprise governance and compliance implications​

Planning Mode’s promise of auditable, incremental edits is attractive to enterprises, but it also raises governance questions that must be answered before large-scale rollout:

• Data residency and model hosting: If your tenant allows routing to third-party models or cloud-hosted model providers, verify hosting terms and cross-cloud data paths. Enterprise admin controls and opt-ins are essential.
• Audit trails and change logs: The plan file + branch + CI logs produce a natural audit trail. Decide whether plan.md files are treated as internal docs or source artifacts and set retention policies accordingly.
• Regulatory and IP concerns: Agent actions that read repositories with regulated data or IP should be restricted. Use policies to whitelist which repos or teams may use Copilot agent features.
• Cost and consumption controls: Agent-driven workflows that rely on paid model inference or Copilot Credits can create new operating costs. Track consumption and set limits for non-production runs.

From a governance viewpoint, Planning Mode adds clarity to the agent’s behavior (plan files help) but also expands the operational surface area (tool calls, model selection, and potential resource creation).

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Adoption roadmap — a recommended phased plan​

• Pilot (2–4 weeks): Choose 2–3 non-critical repositories with good tests. Enable Planning Mode for a small group of developers. Measure task completion rates, iteration counts, and CI failures.
• Evaluate (2–4 weeks): Compare metrics against historical baselines. Validate vendor claims (e.g., SWE-bench improvements) on your own tasks. Assess UX friction like mid-run plan edits and temp-file handling.
• Harden policies (2–6 weeks): Implement branch protection, CI gates, secret-handling rules, model whitelists, and audits. Review service principal scopes for any agent-invoked deployments.
• Scale selectively (ongoing): Extend to teams where automation gives clear ROI (e.g., repeated refactors, library upgrades). Keep sensitive repos restricted. Monitor model consumption and cost.
• Continuous review: Collect developer feedback, track regressions, and refine the planning templates and prompts your teams use (a consistent plan template improves agent performance).

This phased approach balances experimentation and control, letting organizations reap benefits while mitigating operational risk.

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What Planning Mode signals about the future of IDEs​

Planning Mode is part of a larger industry trend turning IDEs into orchestration hubs for AI agents that do more than suggest text. The shift is significant:

• IDEs become control planes: editors will not only show code but will host agent workflows that inspect, plan, and implement changes across a repository.
• Models will be choice-driven: multi-model ecosystems and BYOM (bring-your-own-model) options will demand clearer tenant-level governance and routing rules.
• Auditable agent interactions: visible plan artifacts and working-branch workflows trend toward machine-generated changelogs and more reproducible agent activity.

For developers, that means less manual drudgery for repetitive tasks; for managers and security teams, it means newly important operational controls and audit mechanisms. Planning Mode is an early but meaningful step in redefining what it means for an IDE to collaborate with an AI partner.

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Practical tips for prompt engineering with Planning Mode​

• Use clear, goal-oriented prompts: state the desired end state and acceptance criteria (e.g., "Refactor X to implement exponential backoff, add unit tests that simulate failure, and ensure no public API changes").
• Provide context files or links: the agent performs better when it can locate relevant modules and tests; adding pointers reduces wasted research steps.
• Favor small, repeatable plan templates: standardizing plan structure (title, goals, tasks, open questions) helps the agent produce consistent outputs and makes review easier.
• Keep human checkpoints: require explicit approvals for merges or production deployments triggered by agent plans. This preserves human oversight and legal accountability.

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Conclusion​

Planning Mode in Visual Studio 2022 v17.14 redesigns Copilot from a reactive assistant into a planning and execution partner inside the IDE. By writing, updating, and executing a living markdown plan, Copilot makes its reasoning visible, reduces surprise edits, and shows measurable early gains on multi-step engineering tasks. Those gains come with real operational responsibilities: governance, CI gating, secrets handling, and careful pilot validation are prerequisites for safe adoption.
For teams with mature CI/CD pipelines and robust test coverage, Planning Mode offers a tangible productivity lever. For organizations considering enterprise rollout, a staged adoption plan — pilot, evaluate, harden, scale — will capture value while limiting risk. This feature is a notable milestone in the agentification of development tools, and it foreshadows IDEs that not only assist, but own repeatable engineering tasks under human supervision.

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Source: Windows Report Visual Studio is Gets Copilot-powered Planning Mode