Planning in Visual Studio Copilot: Auditable multi-step task execution

Microsoft has quietly shifted Copilot inside Visual Studio from a prompt-driven assistant into a goal-oriented collaborator by shipping a Planning capability in public preview, giving developers a structured way to manage multi-step, cross-file work and turning ad-hoc prompts into auditable, trackable plans that update as the agent executes them.

Background​

The new Planning feature arrives in Visual Studio 2022 (version 17.14) as a public preview addition to Copilot’s Agent Mode. It is explicitly designed to bridge the gap between single-turn completions and real-world engineering tasks that require research, dependency discovery, testing, and iterative fixes. Instead of returning a single code snippet or a sequence of edits based on one prompt, Copilot can now decide when a task needs a plan, generate a markdown plan file describing the steps it will take, and then execute those steps while updating the plan in real time.
This is not a cosmetic change. It formalizes an intent-first workflow: developers set outcomes and constraints, and Copilot constructs a structured, multi-step roadmap to get there. Under the hood, Planning draws on research in hierarchical planning and closed-loop planning, and integrates with Copilot’s agentic features and the Model Context Protocol (MCP) to access project context and external tools.
The first public preview rollout is gradual and gated behind a user setting: enable it via Tools > Options > Copilot > Enable Planning in Visual Studio 2022 v17.14. The feature writes plan artifacts to a temporary directory by default: %TEMP%\VisualStudio\copilot-vs. Developers who want persistence can move a plan into their repository, enabling history, code review, and CI validation.

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What Planning Does and How It Works​

The planning lifecycle — from intent to execution​

Planning transforms a developer’s high-level request into a living document and an execution trace. The lifecycle looks like this:

• Developer enters a prompt that implies a multi-step task.
• Copilot evaluates whether a simple response will suffice or whether a plan is required.
• If a plan is needed, Copilot generates a markdown plan file that:
• Defines the task and success criteria.
• Lists research/discovery steps (e.g., find usages, identify tests).
• Contains a checklist of implementation tasks with progress markers.
• Copilot executes steps sequentially, updating the plan file as steps start, succeed, or fail.
• The developer can inspect the plan, adjust goals, and—if necessary—stop execution to modify the plan and restart.

This structure gives developers visibility into the agent's reasoning and progress, which is a meaningful shift away from opaque single-shot outputs.

Key technical elements​

• Agent Mode: Planning is built on Copilot’s agentic capabilities. Agent Mode allows the assistant to call tools, run code analysis, write files, and orchestrate multiple actions as part of a single request.
• Model Context Protocol (MCP): MCP helps agent-style tools access richer, structured context (file trees, indexes, external resources) in a predictable way. Planning leverages this to research codebases and produce context-aware plans.
• Plan as a markdown artifact: Storing the plan in markdown makes it human-readable, diffable, and easy to commit to source control if you want persistence.
• Temporary storage by default: Plan files are placed in %TEMP%\VisualStudio\copilot-vs\ to avoid polluting repositories by default. Moving them into the repository is intentional and explicit.

Why structured planning matters​

• Predictability: The plan shows what Copilot intends to do before large-scale edits occur.
• Traceability: A plan file records steps and rationale, providing an audit trail.
• Adaptiveness: Copilot revises plans as it encounters new information or failures, which reduces wasted cycles and repetitive prompt tuning.
• Model-agnostic advantage: Early testing indicates improved performance across different model families when planning is used, not just for a single model—suggesting the pattern generalizes.

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Practical Experience — What Developers Will See​

The UX in Visual Studio​

• When a multi-step prompt is recognized, Copilot will switch from the Ask flow to Agent Mode automatically, and generate a plan accessible as a markdown file.
• The plan updates in real time as tasks execute. Checkboxes, execution logs, and short rationale notes appear inline in the markdown.
• If you edit the plan while the agent is running, changes may not take effect immediately. The current preview requires you to stop the execution, update the plan, and restart for deterministic adoption of edits.

Storage and portability​

• Default path for plans: %TEMP%\VisualStudio\copilot-vs\
• To persist a plan beyond the temporary lifetime, add it to your repository. This enables:
• Versioning and history
• PR-based review of the agent’s intentions and actions
• CI validation before merging generated changes into mainline code

Enabling and trying Planning​

• Update Visual Studio 2022 to v17.14 (public preview channel if necessary).
• Open Tools > Options > Copilot and enable Planning.
• Enter a prompt for a multi-step task in Copilot Chat while in Agent Mode.
• Inspect the generated markdown plan that appears in the temp folder.
• Optionally move the file into a branch in your repo for review and CI.

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Early Results and What They Mean​

Preliminary benchmark runs using SWE-bench—an industry benchmark for AI coding tools—indicated measurable gains: roughly ~15% higher success rates and ~20% more tasks completed when planning was employed versus single-turn prompts. These improvements showed up across multiple models during internal testing, including two distinct families commonly referenced in the field.
Caveat: benchmark improvements are meaningful, but benchmarks are not identical to production complexity. Benchmarks provide controlled scenarios; real-world projects introduce variability such as proprietary frameworks, secrets, custom build systems, and human review processes. Treat the 15–20% figures as promising indicators rather than guarantees.

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Benefits for DevOps, Engineering Teams, and Managers​

• Reduced babysitting overhead: Planning automates research and stepwise execution, allowing engineers to focus on higher-level review rather than constantly nudging the model.
• Better auditability for enterprise: The plan file is a natural place to capture purpose, scope, and the decision rationale—valuable for compliance-sensitive organizations.
• Smoother refactors and migrations: Multi-file refactors and infra changes require discovery and sequenced actions; a coordinated plan reduces the chance of missing dependent edits.
• Improved reproducibility: When you commit a plan to git, you enable reproducible agent runs tied to a specific branch, commit, or PR.
• Faster onboarding and knowledge transfer: Plans can document why a change is needed and how it will be performed—useful when multiple engineers collaborate or hand off work.

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Risks, Limitations, and Security Considerations​

1. Temporary storage and data exposure​

Plans live in a system temporary folder by default. That reduces accidental repository leakage, but it also creates transient artifacts that may contain code snippets, file paths, or commentary about sensitive areas of your project. If you move a plan into your repository for persistence, that action must be deliberate because it creates a permanent record that could inadvertently include sensitive information.

• Risk: Committing plan files with secrets, API keys, or other sensitive info.
• Mitigation: Run a secrets scanner on plan files before adding them to a repo. Use pre-commit hooks and CI scanning.

2. Model trust and correctness​

Planning improves reliability, yet the agent still depends on underlying models that can hallucinate, misinterpret intent, or produce insecure changes.

• Risk: Faulty or insecure code changes being inserted across multiple files.
• Mitigation:
• Require PRs for plan-applied changes.
• Use CI pipelines with unit tests, static analysis, and security scans before merge.
• Add human review gates for high-risk repos (infrastructure, authentication, cryptography).

3. Mid-response edit semantics​

Editing a plan while Copilot is executing can lead to race conditions: edits may not be picked up immediately, and stopping+restarting is currently necessary for consistent behavior.

• Risk: Inconsistent plan state and unexpected agent behavior if edits are applied mid-run.
• Mitigation: Adopt a workflow of stopping the agent, applying edits, and restarting; treat plans as the canonical specification while edits are staged to avoid race conditions.

4. Audit and governance complexity​

Persisting and sharing plans makes Copilot’s reasoning discoverable—but it also requires governance.

• Risk: Multiple developers running and committing divergent plans could create fragmentation or conflicting change strategies.
• Mitigation:
• Use branches/PRs and central review.
• Define policies for when plans can be committed (e.g., only to feature branches, not main).
• Maintain a simple checklist for plan review before merge.

5. Supply-chain and external model dependence​

If your Copilot configuration uses external models or private model endpoints, planning may trigger a sequence of external API calls and data transmission.

• Risk: Confidential code fragments or telemetry being sent to third-party model providers.
• Mitigation:
• Understand your Copilot account configuration and model endpoints.
• Prefer enterprise-controlled model deployments when dealing with sensitive code.
• Audit network egress and set policies for what can be processed by external models.

6. False sense of automation​

Planning can make Copilot seem like an autonomous developer that “just gets things done.” That illusion can encourage over-reliance.

• Risk: Reduced human scrutiny, leading to technical debt or missed system-level considerations.
• Mitigation:
• Keep humans in the loop for design-level decisions.
• Use Planning to generate candidate solutions, but require human sign-off for architecture-level changes.

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Best Practices and Recommendations​

Adopting Planning in Visual Studio should be deliberate. These pragmatic recommendations are designed to help teams extract value while minimizing risk.

Getting started (short checklist)​

• Enable Planning in a non-production environment first (Tools > Options > Copilot > Enable Planning).
• Use Planning on small, well-scoped tasks to learn the lifecycle.
• Keep plan files in temp until you are confident with the artifact content.
• Run a secrets scan on any plan before committing it.

Recommended engineering workflow​

• Create a feature branch for agent-driven changes.
• Invoke Planning in Agent Mode and allow Copilot to create the markdown plan.
• Inspect the plan and edit success criteria or constraints if needed.
• Commit the plan to the feature branch (optional) and open a PR that includes the plan plus proposed code changes.
• Run CI with unit tests, integration tests, static code analysis, and security scans.
• Apply human review gates for architectural or security-sensitive PRs.
• Merge only after passing CI and review.

Governance and policy suggestions​

• Define a policy for when plan files may be committed (e.g., permitted to feature branches only).
• Require secrets scanning on commits that include plan files.
• Maintain an agent-run log policy: keep plan files for audit purposes but limit retention as required by privacy/maintenance needs.
• Educate teams on how to interpret plan artifacts and on the need for human oversight.

Tooling and automation suggestions​

• Add pre-commit hooks to reject commits containing credentials or large inline data dumps from plan files.
• Integrate plan scanning into CI for automated checks.
• Build a lightweight reviewer checklist that includes:
• Are goals correctly expressed and bounded?
• Are dependencies and side effects identified?
• Are tests and rollback plans included?

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For DevOps Teams — Specific Considerations​

Infrastructure as code and pipeline changes​

When Planning touches CI/CD pipelines or IaC, the stakes are higher. Multi-step changes often include secrets, environment configuration changes, and timing concerns.

• Use ephemeral test environments to validate pipeline changes created by Copilot.
• Require manual promotion for any pipeline or IaC change that impacts production.
• Ensure secret rotation and vaulting practices are in place; never store secrets in plan artifacts.

Release management​

• Treat agent-generated changes like any other PR: include release notes and rollback instructions.
• Use blue/green and canary deployments where possible to limit blast radius.

Observability and tracing​

• When Planning executes sequences that modify application behavior, ensure you have observability instrumentation in place before merging to main to rapidly detect regressions.

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The Road Ahead — Where Planning Might Lead​

Planning in Visual Studio is positioned as a foundation for more advanced agent-driven workflows. Possible next steps Microsoft appears to be exploring include:

• Longer-term plan storage and teamwork features: built-in plan repositories, plan sharing, and plan history to improve team collaboration.
• Richer model-context integration: deeper project indexing and caching so plans can be more context-aware and faster.
• Smarter mid-execution editing: more granular checks that let edits propagate during runs without forcing a full stop/restart.
• Policy-controlled execution: organizational controls over what an agent can or cannot modify, based on repo rules or tag-based governance.
• Model diversification and plugin tooling: the ability to orchestrate multiple specialized models or private model endpoints for safer, domain-specific planning.

Expect Microsoft to iterate quickly on caching and reasoning improvements, and to expand integrations with Visual Studio’s existing testing and code quality tooling. The vision being signaled is planning-driven development: developers set intent and the ecosystem coordinates the “how.”

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Critical Analysis — Strengths, Weaknesses, and Strategic Impact​

Strengths​

• Transparency: Plans expose the agent’s intended workflow, addressing a long-standing critique of black-box code generation.
• Scalability: The planning approach scales better for large, interconnected tasks where single-shot outputs struggle.
• Enterprise alignment: Auditable markdown plans and the ability to persist them align with enterprise governance and compliance needs.
• Model-agnostic gains: Early tests suggest planning helps multiple model families, implying broader utility rather than a narrow optimization.

Weaknesses and open questions​

• Operational friction: The stop-edit-restart pattern for mid-execution edits is a UX friction that could slow iterative work.
• Data governance complexity: Temporary files mitigate accidental commits but do not eliminate the risk of leakage when plans are intentionally persisted.
• Benchmark vs. reality gap: Performance improvements measured on test suites may not map directly to the messy dependencies, proprietary libs, and social workflows in real projects.
• Over-automation risk: The more the workflow looks like “set and forget,” the greater the chance teams will under-invest in review, testing, and observability.

Strategic impact​

Planning shifts the narrative from code generation to outcome generation. This is a strategic inflection point for IDE-based AI assistants: the product ambition is no longer just "help me write code" but "help me achieve an outcome reliably and audibly." For organizations, that means re-evaluating developer workflows, tooling, and governance—planning is a force multiplier if harnessed properly, and a new failure mode if treated as a magic button.

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Final Recommendations​

• Treat Planning as a powerful experimental tool: start small, stay deliberate, and enforce review pipelines.
• Add secrets scanning and CI gating to any flow that persists plan files to source control.
• Keep humans responsible for architecture decisions and high-risk merges; use Planning to accelerate discovery and routine refactors.
• Invest in observability and test coverage before letting Planning run across production-impacting subsystems.
• Monitor Microsoft’s updates to caching, mid-execution semantics, and persistence options, and be ready to adapt governance policies accordingly.

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Planning in Visual Studio Copilot is a substantive evolution: it brings structure, traceability, and a new level of agency to AI-assisted development. For teams willing to invest in governance and CI hygiene, it can reduce friction on complex, multi-file tasks and make agent-driven work auditable and reviewable. For teams that skip these guardrails, it introduces new operational and security risks. The smart path is measured adoption—use Planning to accelerate routine, well-scoped engineering work while tightening review and testing practices before letting it touch mission-critical systems.

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Source: DevOps.com Visual Studio Copilot Gets Planning Mode for Complex Tasks - DevOps.com