Cursor • Fix Agent Failures with Pre-Planning

Stop duplicate files, wrong paths, and scope creep. Plan first, execute safely.

Cursor Agent is the execution environment. PlanToCode is the explicit reviewable plan that prevents common Agent failures: duplicate files, wrong imports, missing dependencies, scope creep. Intelligence-Driven Development in 5 stages.

Stages 1-4: Spec capture (voice/text) → FileFinderWorkflow (4 stages) → Multi-model planning (GPT-5.2/Claude Sonnet 4.5/Gemini) → Human review and merge. Stage 5: Cursor Agent executes the blueprint. No surprises.

Cursor Agent Terminal and Background Agents plan internally during execution—but without pre-discovery, multi-model synthesis, or human review, they fail predictably: duplicate files, wrong paths, missing imports, scope creep. PlanToCode fixes this with Intelligence-Driven Development: (1) Specification capture via text_improvement/task_refinement, (2) Targeted file discovery via 4-stage FileFinderWorkflow, (3) Multi-model implementation planning across GPT-5.2/Claude Sonnet 4.5/Gemini, (4) Human review and plan merge, (5) Cursor Agent executes the merged blueprint. All claims verified against Cursor's official documentation.

What PlanToCode gives you here

Stop duplicate files and wrong paths

Common Cursor Agent failure: creates components/Button.tsx when src/components/Button.tsx exists. Stage 2 FileFinderWorkflow (root folder selection, regex file filter, AI relevance assessment, extended path finder) discovers exact paths before planning. Stage 4 review validates every path. Stage 5 Cursor Agent gets pre-resolved paths—no duplicates.

Stop wrong imports and missing dependencies

Cursor Agent improvises imports during execution, misses transitive dependencies. Stage 3 multi-model planning with GPT-5.2 and Gemini surfaces different dependency graphs. Stage 4 merge consolidates them. Cursor executes with complete import map—no runtime failures.

Large refactor scenario: 40 files, multi-model safety

Refactor auth system across 40 files (routes, middleware, components, tests, docs). Stage 2 FileFinderWorkflow discovers all 40. Stage 3 generates plans from GPT-5.2 (backward-compatible) and Gemini (new patterns). Stage 4 merge prioritizes GPT-5.2 rollback, Gemini test coverage. Stage 5 Cursor Agent executes with full context—no stray edits.

Human review prevents scope creep

Cursor Agent often expands scope mid-execution (rewrites unrelated code, adds features). Stage 4 human review locks scope: approve/reject each file edit, constrain boundaries. Cursor sees only approved actions. The skyscraper blueprint defines every floor, wall, door—no improvisation.

Multi-model blind spot prevention

Single-model Cursor runs miss edge cases (GPT-5.2 misses new patterns, Gemini misses legacy constraints). Stage 3 runs implementation_plan across 3+ models. Stage 4 merge surfaces conflicts, you choose. Cursor executes the synthesized plan—comprehensive, not narrow.

Architect vs construction crew analogy

PlanToCode is the architect (Stages 1-4): capturing requirements, surveying site (file discovery), drafting blueprints (multi-model), reviewing plans (merge). Cursor Agent is the construction crew (Stage 5): building exactly what the blueprint specifies. No architect = chaotic construction.

How it works with this tool

Large refactor scenario: 40-file auth system overhaul

Without PlanToCode: Cursor Agent rewrites src/auth/login.ts, misses lib/auth/login.ts (duplicate), breaks imports in 12 downstream files, adds unplanned OAuth flow (scope creep), fails tests. With PlanToCode: Stage 2 FileFinderWorkflow discovers both login.ts files, all 40 affected files, transitive dependencies. Stage 3 generates plans from GPT-5.2 and Gemini—each with a different approach. Stage 4 review rejects Gemini OAuth suggestion (out of scope), merges remaining. Stage 5 Cursor Agent executes merged blueprint—no duplicates, no scope creep, tests pass.

How Stage 2 path validation prevents duplicate files

FileFinderWorkflow Stage 2a (root folder selection): Identifies src/ vs lib/ vs packages/. Stage 2b (regex file filter): Matches **/*auth*.ts, **/*login*.ts. Stage 2c (AI relevance assessment): Scores each file's relevance to "refactor auth system." Stage 2d (extended path finder): Discovers transitive imports (components using auth, tests, docs). Human review confirms all paths. Cursor Agent sees validated path list—creates no duplicates.

How Stage 4 merge resolves import conflicts

GPT-5.2 plan: Import { auth } from "@/lib/auth" (absolute). Gemini plan: Import { auth } from "../lib/auth" (relative). Stage 4 merge instruction: "Use absolute imports per project convention." Merged blueprint specifies @/lib/auth everywhere. Cursor Agent follows merged imports—no mismatches, no runtime errors.

Cursor Agent Terminal vs Background Agents execution

Agent Terminal: Executes within Cursor IDE, inherits workspace context, suitable for interactive tasks. Background Agents: Isolated ubuntu-based VMs, configurable via environment.json, suitable for long-running builds/migrations. Both consume the same PlanToCode merged blueprint (Stages 1-4 output). Choose execution environment based on task duration and isolation needs.

Pre-resolved imports and dependency graph

Stage 3 multi-model planning: GPT-5.2 identifies direct imports (auth → user → db), Gemini identifies transitive imports (auth → session → cache → redis). Stage 4 merge consolidates full dependency graph. Merged blueprint includes import statements, dependency order, circular dependency warnings. Cursor Agent executes in correct order—no missing imports, no circular failures.

PlanToCode + Cursor workflow: From voice to execution

Stage 1: Record voice note "Refactor auth to support SSO." Use text_improvement prompt to clarify, task_refinement to break down. Stage 2: Run FileFinderWorkflow, discover 40 files. Stage 3: Generate GPT-5.2 plan (safe), Gemini plan (fast). Stage 4: Review side-by-side, merge with instructions. Stage 5: Copy merged XML to Cursor Agent, execute. PlanToCode terminal logs progress, health checks detect failures, auto-reconnect if Cursor crashes.

Quickstart

1
Install PlanToCode on the same machine as Cursor
Download PlanToCode for macOS, Windows, or WSL and connect it to your repository.
2
Run file discovery for your task
Generate a focused set of files and dependencies that Cursor Agent will need to consider.
3
Generate and merge plans
Create implementation plans from multiple AI models and merge them into a comprehensive, Cursor-ready specification.
4
Execute with confidence
Provide the plan to Cursor Agent Terminal or Background Agents, or run it in PlanToCode's terminal with approvals and full logging.

Learn more:Agent Terminal·Pricing

Verified from official sources

Cursor Agent Terminal provides AI-powered command execution directly in the IDE

Official docs

Background Agents run tasks in isolated virtual machine environments

Official docs

Cursor provides Composer mode for AI-assisted code generation

Official docs

Cursor indexes your codebase for context-aware suggestions

Official docs

Cursor CLI enables terminal-based interactions with AI features

Official docs

Cursor Agent Terminal natively executes commands in the IDE

Official docs

Background Agents run on ubuntu-based machines

Official docs

Cursor provides pricing tiers for different usage levels

Official docs

Frequently Asked Questions

Everything you need to know about PlanToCode

Yes. PlanToCode provides a human-in-the-loop workflow where team leads and stakeholders can review generated implementation plans, edit details, request modifications, and approve changes before they are executed by coding agents or developers. This ensures corporate governance and prevents regressions.

Upload Microsoft Teams meeting recordings or screen captures to PlanToCode. Advanced multimodal models analyze both audio transcripts (including speaker identification) and visual content (shared screens, documents) to extract specification requirements. You review the extracted insights - decisions, action items, discussion points - and incorporate them into implementation plans.

Yes. Implementation plans break down changes on a file-by-file basis with exact repository paths corresponding to your project structure. This granular approach ensures you know exactly what will be modified before execution, providing complete visibility and control.

PlanToCode is a desktop planning workspace. You run the file discovery workflow, review implementation plans in a Monaco editor, adjust prompts, and then launch the terminal from the same session. Chat tools hand you a single reply; here you stage the work before anything runs.

Yes. The app assumes you already work in large repositories and are comfortable with terminals, dependency graphs, and architecture trade-offs. The software keeps context organized so you can apply your judgment faster.

The default configuration ships with Gemini 3 Pro, GPT-5.2, Claude 4.5 Sonnet, o3, Grok 4, DeepSeek R1, and Kimi K2. You can switch models per task from the settings panel before submitting a job.

Project state, terminal logs, and plan drafts are stored locally in SQLite. Only the prompt you approve plus the files you select are sent through your configured server proxy. The Rust server in this repo can be self-hosted when you need to keep traffic on your own infrastructure.

The terminal boots the same shell you use on the host machine and auto-detects claude, cursor, codex, and gemini binaries. You can still run any other tooling manually - the PTY session is a regular shell with voice dictation available when you need hands-free input.

Start the file finder workflow to generate search patterns, relevance scoring, and prioritized file lists. Each stage runs as its own background job so you can inspect results before applying the selections to your session.

Absolutely. Plans open in a Monaco editor with syntax highlighting, diff-friendly formatting, and clipboard helpers. You can tweak the prompt, copy individual steps, and rerun generation without leaving the workspace.

Select two or more plans, write the merge guidance, and submit. The merged result is saved alongside the originals so you can compare drafts, keep notes, or rerun the merge with different instructions.

Token estimates, prompt previews, terminal health monitoring, and persistent sessions reduce rework. You decide when to run commands and which model to trust, while the app handles orchestration details.

Describe the task (typing or voice) → run the file finder workflow → generate one or more implementation plans → edit or merge plans in the Monaco editor → launch the built-in terminal to execute the plan and monitor logs. Sessions and logs stay available the next time you open the app.

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Plan software changes before you code. Review scope, merge multi-model insights, and execute with full visibility.

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