Google has added a planning mode to Antigravity, its AI coding agent, that requires the agent to research a task, draft an implementation plan, and wait for human approval before writing any code. The feature, triggered with the /plan command, shipped on September 25, 2026, and is now available in both the desktop app and command-line interface.

This move reflects a broader shift in how AI coding tools are being designed. Rather than letting agents autonomously write and test code, teams are building in explicit approval gates to catch mistakes early, reduce token waste, and create a reviewable record of what the agent intended to do.

Why Is Planning Mode Becoming Standard for AI Coding?

Unconstrained AI agents can burn through tokens, hide their reasoning from developers, and merge unreviewed code changes without explanation. Planning mode addresses all three problems by forcing the agent to think through the task upfront, document its approach, and wait for a human thumbs-up before touching any files.

The research shows that upfront planning can match step-by-step accuracy on structured tasks while using fewer AI model calls, because the model reasons once instead of re-deriving strategy every time it uses a tool. For teams working on sensitive code, unfamiliar codebases, or systems with large blast radius, this approval gate is no longer a nice-to-have; it is becoming expected behavior.

How Does Antigravity's /plan Command Actually Work?

• Research Phase: The agent reads your codebase, documentation, and relevant web resources to understand the full context before proposing changes.
• Plan Artifact: Instead of immediately editing files, the agent outputs a structured implementation plan for your review, including scope, non-goals, files to touch, tests to add, and rollback steps.
• Approval Gate: Execution begins only after you explicitly approve the plan, reject it, or ask for edits.
• Natural Language Alternative: You can also ask for a plan in plain language without the slash command for a lighter planning pass that does not enforce the same hard gate.

Google emphasized that the desktop app and CLI share the same /plan flow, not a CLI-only afterthought. This parity matters for teams that script agents in continuous integration pipelines or remote development boxes, where terminal-based workflows are the norm.

What Is the Difference Between /plan and Other Antigravity Commands?

Antigravity now has multiple command modes, each optimized for different scenarios. The /plan command is the human-gated front of the pipeline, designed for governance and clarity at task start. By contrast, the /boost command optimizes for deep reasoning and evidence at task end, using execute-verify loops to solve hard problems.

The /teamwork command handles multi-agent campaigns that can run for hours, with milestone reviews and parallel agents working on different parts of a task. Neither /plan nor /boost replaces the other; they serve different use cases.

When Should Teams Use /plan Versus Other Modes?

• Use /plan: When the blast radius is large, the codebase is unfamiliar, or compliance requires a reviewable artifact before changes merge. Examples include adding OAuth2 login, refactoring payment systems, or modifying authentication logic.
• Use /boost: When you need deep reasoning on a hard problem and are willing to let the agent loop through execute-verify cycles to find the right answer, even if it takes more tokens.
• Use /teamwork: When a task is complex enough to benefit from multiple specialized agents working in parallel, each narrowly focused to reduce hallucination from context overload.
• Use natural-language planning: For medium-complexity tasks where you want orientation and a soft gate without formally invoking /plan mode.

The distinction matters because a great plan with a weak executor still fails integration tests. When evaluating Antigravity against competitors like Claude Code or Cursor, score the planning feature and the underlying model together.

What Are the Real-World Benefits for Development Teams?

Planning mode creates several practical wins. Staff engineers and tech leads get a forced articulation step before junior developers merge agent output; the plan becomes a teaching artifact that explains why the agent chose a particular module structure or test shape. Regulated or customer-facing teams can attach approved plans to change records without screenshotting chat bubbles, creating an auditable trail. Solo builders still benefit when returning to a stale branch; the plan is cheaper session memory than re-ingesting fifty tool messages.

For teams already documenting harness engineering practices, adding /plan transcripts to the definition of done for high-priority changes is straightforward. The plan becomes part of the code review, not a separate artifact.

What Are the Limitations and Risks?

• Approved Plans Can Still Be Wrong: Approval means you accepted the approach, not that the code is safe or will pass all tests. The plan is a communication tool, not a guarantee.
• Research May Hit the Web: If the agent browses the internet during the research phase, treat /plan like any agent with browsing capabilities. Align with security lessons from similar tools if research fetches untrusted URLs.
• Injection via External Requirements: If the agent ingests user-provided specifications from issues or tickets, pair planning with behavior classifiers or manual review to catch malicious or incorrect requirements.
• Latency for Time-Critical Fixes: One-line typos, config tweaks, and incident hotfixes may not benefit from planning mode if the latency of generating a plan exceeds the urgency of the fix. Keep faster paths available for those scenarios.

Blocking writes until approval reduces drive-by diffs, a common failure mode when developers accept the first patch because the agent "already started".

How Does Antigravity's Planning Mode Fit Into Google's Broader AI Coding Strategy?

Antigravity is Google's agent-first IDE, and /plan is a sign of maturity in the harness layer. It is orthogonal to which Gemini model revision powers the agent; whether Google ships Gemini 4 or adds new post-training techniques, the planning contract remains the same.

At Google I/O 2026 in May, Google announced Gemini 3.5 Flash, a fast, low-cost model focused on agentic coding and long-horizon tasks, available through the Gemini API, Google AI Studio, Android Studio, and Antigravity. Jules, Google's async coding agent, is now in general availability with GitHub integration and supports Python, TypeScript, JavaScript, Go, Rust, and Java. The Agent Development Kit (ADK) 1.0 reached general availability with multi-language support, including an Agent2Agent protocol that treats long-running agent tasks as first-class objects with real-time progress streaming.

Planning mode is the human-facing side of this infrastructure. It ensures that as agents become more capable and autonomous, teams retain visibility and control over what the agent intends to do before it does it.

How to Implement Planning Workflows in Your Development Process

• Test on a Real Ticket: Run /plan on a non-trivial task you would normally hand to an agent cold. Compare the files touched and surprises versus your previous workflow to understand the planning overhead and value.
• Compare Planning Depths: Run the same task with natural-language planning only, without the /plan slash command. Note whether research depth and plan quality differ, and decide which mode fits your team's risk tolerance.
• Document the Plan as Code Review: Treat the approved plan as part of the pull request description or commit message. Link the plan artifact to the change record so future maintainers understand the original intent.
• Set Approval Policies: For high-risk directories (payments, authentication, infrastructure), require plan logs in ticket comments before merge. For low-risk changes, allow natural-language planning without the formal /plan gate.
• Track Metrics: Measure plan-to-merge time and revert rate. A cheap planning pass that prevents one bad merge pays for itself in reduced incident response and debugging time.

Teams on self-hosted or multi-vendor stacks can copy the same contract: artifact (Markdown plan with scope and rollback), gate (CI rejects agent writes until plan is approved), versioning (plan v2 when scope changes), and metrics (track plan-to-merge time versus revert rate).

The planning mode is the same planner-worker-critic decomposition that harness engineers have been building manually for years; Antigravity just named it, slash-commanded it, and made it a first-class feature in the IDE.