Verdent is an AI-powered development environment designed for real development workflows where you’re rarely working on a single task. Run multiple AI agents simultaneously across isolated workspaces, delivering production-ready code even when you’re away.

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What You’ll Learn

  • Core workflow capabilities (Plan Mode, Parallel Agents, Workspace Isolation)
  • Context awareness and specialized sub-agents
  • Collaboration modes and extensibility
Verdent’s workflow is built around three core phases:
  • Execute - Run multiple agents in parallel across isolated workspaces
  • Isolate - Each agent works in its own workspace with full file isolation
  • Review - Compare and rebase results
These features work together to enable parallel development with full control.

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Core Workflow Features

Plan Mode

Capture requirements and break down tasks with AI before generating code

Parallel Execution

Run multiple AI agents simultaneously across different tasks, reducing turnaround time

Code Review

Receive structured, contextual feedback and improvement suggestions

Multitasking

Handle multiple tasks within the same workspace with quick switching

Workspace Isolation

Each workspace is a completely isolated environment using git worktrees

Project Switching

Jump between projects instantly while keeping all workspace states alive

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Additional Capabilities

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Context Awareness: Deep Codebase Understanding

Verdent’s context management system enables comprehensive project comprehension:

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Massive Context Window

  • Up to 1M Token Capacity - Standard models support 200K tokens; Claude Sonnet 4.5 1M extends to 1M for larger codebases
  • Smart Context Loading - Automatically prioritizes relevant files based on task context
  • Sub-Agent Context Optimization - Delegates specialized tasks to focused sub-agents

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Adaptive Learning

  • Convention Detection - Learns project-specific patterns (naming, file organization, error handling)
  • Style Mimicry - Generates code matching existing style (indentation, brace placement, comments)
  • Library Awareness - Recognizes frameworks in use, preferring them over new dependencies

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Cross-File Coherence

  • Dependency Tracking - Understands imports, exports, and module relationships
  • Impact Prediction - Identifies components affected by proposed changes
  • Consistency Enforcement - Ensures modifications align with existing architecture

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Specialized Sub-Agents: Division of Labor

Verdent orchestrates specialized AI agents optimized for specific development tasks:
Purpose: Rapid code quality checks and validationCapabilities:
  • Lint Checks - ESLint, Pylint, Rubocop, etc.
  • Type Validation - TypeScript, mypy, Flow type checking
  • Fast Test Execution - Targeted unit tests with under 30s budget
  • Diff-Focused Verification - Validate only changed code for efficiency
Fail-Fast Philosophy: Returns structured error reports on first real issue, avoiding time wasteUse Cases: Pre-commit checks, post-fix validation, quick sanity tests

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Flexible Collaboration Modes

Choose the level of autonomy that fits your workflow:
  • Agent Mode - Executes tasks autonomously with full file modifications and command execution
  • Plan Mode - Read-only mode for analysis and planning without file modifications
See Execution Modes & Permissions for detailed mode documentation.

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MCP (Model Context Protocol) Integration

Enables interoperability with external tools and services:
  • Extends functionality through existing toolchains and custom plugins
  • Works seamlessly with sub-agents to support distributed task execution
  • Supports integration with external APIs, databases, and development tools
See MCP Integration for setup and configuration.

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Additional Features

Precise Context Control:Attach specific files, folders, or code sections directly in chat using @ mentions to provide targeted context for AI assistance.How It Works:
  • Type @ in chat to see a list of available files and folders
  • Select specific files to include in the conversation context
  • Reference entire directories for broader context
  • Mention specific code sections or documentation pages
Use Cases:
  • Focus AI on specific modules when debugging
  • Include configuration files when discussing setup
  • Reference related components when implementing features
  • Provide documentation context for accurate guidance

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See Also

Getting Started

Start using Verdent in minutes

Subagent Management

Configure specialized sub-agents

User Interface Overview

Learn the Verdent interface

Parallel Execution

Deep dive into parallel execution