OpenWriterControle/docs/features/agent-system-blueprint.md

The OpenCode Agent System Blueprint

Build Intelligent Workflow Systems with Context-Aware AI

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📘 What Is This Blueprint?

This is a teaching document that explains how the OpenCode agent system works and how to extend it for your needs.

You don't need to read this to use the system - the agents work out of the box. Read this document only if you want to:

• Understand the architecture behind the system
• Create your own custom agents and commands
• Extend the system with domain-specific patterns
• Learn how context loading and agent coordination works

If you just want to start building, skip this document and use openagent instead. See the README.md for quick start instructions.

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⚡ TL;DR - Quick Reference

For New Users:

• Start with opencode --agent openagent for all questions and tasks
• The agent handles planning, implementation, testing, and review automatically
• Add your coding patterns to .opencode/context/project/project-context.md
• Let the agent delegate to specialized subagents when needed

For Advanced Users Building Custom Agents:

• Use /prompt-enchancer to create complex agents with workflows
• Read this document to understand architecture patterns
• Learn how context loading works (the @ symbol)
• Extend the system with domain-specific context files

Core Concept:

Commands load context → Agents execute with that context → Subagents handle specialized tasks

When to read this document:

• ✅ You want to create custom agents or commands
• ✅ You need to understand how context loading works
• ✅ You want to extend the system for your specific needs
• ❌ You just want to start building (use openagent instead)

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🎯 What This Document Teaches

This blueprint explains the architecture patterns behind the OpenCode agent system. You'll learn:

• How context loading works (the @ symbol)
• How agents, commands, and context files work together
• How to create custom agents and commands
• How to extend the system for your needs

📖 Installation & Usage: See README.md in the repository root.

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Understanding Examples in This Document

When you see commands like /workflow, /plan-task, /create-frontend-component:

• These are pattern examples showing how you COULD structure commands
• Most aren't implemented in the repository
• The existing openagent and opencoder already handle these workflows
• Create them only if you have specific repeated patterns

When you see extensive context hierarchies:

• The repository includes core/ and project/ context files
• Add domain-specific files (frontend/, backend/, database/) as needed
• Start simple, expand based on your needs

When you see task management structures:

• The task-manager agent creates tasks/ directories automatically
• openagent creates session files in .tmp/sessions/ for context preservation
• No need to pre-create structures

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The Golden Rule

Context flows in one direction: Commands load context immediately, Agents can look up additional context deterministically.

Think of it like a well-organized library: the librarian (command) brings you the right books immediately, but you (agent) can look up specific references when needed.

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How @ Symbol Context Loading Works

The Magic of Automatic Context Injection

When you create a slash command with @ references, OpenCode automatically loads that context into the agent's memory BEFORE the agent starts thinking.

Example command structure:

• Command file references context files using @.opencode/context/...
• OpenCode reads those files automatically
• Context is injected into the agent's working memory
• Agent receives: user request + all loaded context + instructions
• Agent can immediately use patterns without looking them up

Why This Is Powerful

Without @ loading:

• Agent doesn't know your patterns
• Has to search or use generic patterns
• Inconsistent results, slower execution

With @ loading:

• Agent has your patterns loaded immediately
• Follows your exact standards
• Consistent, fast, high-quality results

Best Practices for Context Loading

1. Load 2-4 context files maximum - Prevent cognitive overload
2. Always include core patterns - Essential patterns every agent needs
3. Load domain-specific patterns - Based on what the command does
4. Keep context files focused - 50-150 lines each
5. Use conditional loading - Load different context based on request analysis

Why This Architecture Matters

• It's the foundation of how agents get consistent context
• It determines command design - You must anticipate what context agents need
• It explains the architecture - Commands are "smart loaders", agents are "focused executors"
• It guides best practices - Load the right context, not too much, not too little

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Core Principles

1. Single-Level Context Loading

OpenCode processes @ references only in command templates, NOT recursively in file contents.

• ✅ Works: @ references in command files
• ❌ Doesn't work: @ references inside context files (treated as plain text)

Implication: Commands must load ALL necessary context upfront. Agents can look up additional files using tools (read, grep, glob), but cannot use @ loading themselves.

2. Deterministic vs Non-Deterministic Behavior

• Commands: Non-deterministic - Analyze requests and load appropriate context
• Agents: Deterministic - Predictable behavior, can look up additional context

3. Context Optimization

• Maximum 4 context files per command - 250-450 lines total
• 50-150 lines per context file - Optimal range
• Always load core patterns - Plus request-specific context

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The System Components

1. Commands (.opencode/command/)

What they do: Entry points that load context based on request analysis

Examples:

• /commit - Smart git commits
• /optimize - Code optimization
• /test - Testing workflows
• /prompt-enchancer - Improve prompts and create complex agents

How they work:

• User types a command
• Command analyzes the request
• Loads appropriate context files using @
• Passes everything to an agent
• Agent executes with full context

2. Agents (.opencode/agent/)

What they do: AI workers with specific capabilities and predictable behavior

Main agents in this repo:

• openagent - Universal coordinator for general tasks, questions, and workflows (recommended default)
• opencoder - Specialized development agent for complex coding and architecture
• system-builder - Meta-level generator for creating custom AI architectures

Subagents (specialized helpers):

Core Coordination:

• task-manager - Task breakdown and planning
• documentation - Documentation authoring

Code Specialists:

• coder-agent - Quick implementations
• reviewer - Code review and security
• tester - Test creation and validation
• build-agent - Type checking and validation
• codebase-pattern-analyst - Pattern discovery

Utilities:

• image-specialist - Image generation (Gemini AI)

System Builder (Meta-Level):

• domain-analyzer - Domain analysis
• agent-generator - Agent generation
• context-organizer - Context organization
• workflow-designer - Workflow design
• command-creator - Command creation

Agent structure:

• Frontmatter with metadata (description, mode, tools, permissions)
• Clear instructions for behavior
• Specific rules and constraints
• Structured response formats

3. Context (.opencode/context/)

What it is: Layered knowledge system with your coding patterns

Structure:

• core/ - Essential patterns (always loaded)
• project/ - Your project-specific patterns
• Add more as needed: frontend/, backend/, database/, etc.

How it works:

• Context files contain your coding standards
• Commands load relevant context using @
• Agents follow those patterns automatically
• Single-level loading (no recursive references)

Best practices:

• Keep files focused (50-150 lines)
• Use clear pattern names
• Include when to use each pattern
• Add rules and constraints

4. Task Management (tasks/)

What it is: File-based progress tracking with checkboxes

Structure:

• features/ - Feature development
• fixes/ - Bug fixes
• improvements/ - Code improvements
• single/ - Simple tasks

How it works:

• task-manager creates task files automatically
• Each task has a plan with checkboxes
• Agents update progress as they work
• Quality gates ensure standards are met

5. Workflow Orchestration

How agents coordinate:

• Simple tasks (< 30 min) - Direct execution
• Medium tasks (30min-2hrs) - Task planning with tracking
• Complex tasks (> 2hrs) - Multi-phase with quality gates

Quality gates:

• Build validation (TypeScript, linting)
• Code review (security, quality)
• Testing (automated tests)
• Post-flight review (compliance check)

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System Flow

Simple workflow:

1. User makes request
2. Agent analyzes and plans
3. User approves
4. Agent implements step-by-step
5. Validation runs automatically
6. Complete

Complex workflow:

1. User makes request
2. Agent delegates to @task-manager
3. Task manager creates detailed plan
4. Agent implements one step at a time
5. Progress tracked in task files
6. Quality gates at milestones
7. Subagents handle specialized work
8. Post-flight review
9. Complete

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Context Loading Strategy

Dynamic Context Loading

Commands can load different context based on request analysis:

• Analyze the user's request
• Determine domain (frontend, backend, database, etc.)
• Load base context (core patterns)
• Load domain-specific context conditionally
• Pass everything to agent

Context Size Guidelines

• ✅ Optimal: 50-150 lines (focused, actionable)
• ⚠️ Acceptable: 150-250 lines (comprehensive but manageable)
• ❌ Too Large: 250+ lines (split into focused files)

Context Loading Rules

1. Always load core patterns
2. Maximum 4 context files per command
3. Load based on request analysis (dynamic, not static)
4. Use bash commands for conditional loading

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Building Your Own Agents

Use /prompt-enchancer

If you're building custom agents with complex workflows, use the /prompt-enchancer command:

• Helps you create well-structured agent prompts
• Guides you through best practices
• Ensures proper formatting and instructions
• Creates agents that work well with the system

How to use it:

/prompt-enchancer "I want to create an agent that does X"

The command will help you:

• Structure your agent properly
• Add appropriate context loading
• Define clear instructions
• Set up permissions and tools
• Create workflow patterns

Agent Design Best Practices

1. Make agents deterministic - Predictable behavior
2. Give clear, direct instructions - Not documentation
3. Separate concerns - One agent, one responsibility
4. Use structured response formats - Consistent output
5. Define permissions - Control what agents can do
6. Specify tools - Only give access to what's needed

When to Create Custom Agents

Create custom agents when:

• You have repeated workflows
• You need specialized behavior
• You want domain-specific expertise
• You have unique quality requirements

Don't create custom agents when:

• openagent or opencoder already handles it
• It's a one-time task
• You're just starting out

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Extending the System

Adding Domain-Specific Context

1. Create context directory for your domain
2. Add pattern files (50-150 lines each)
3. Reference in commands using @
4. Agents automatically use your patterns

Example domains:

• Frontend (React, Vue, etc.)
• Backend (APIs, servers)
• Database (queries, schemas)
• Testing (unit, integration)
• Security (auth, validation)

Creating Custom Commands

1. Identify repeated workflows
2. Determine what context is needed
3. Create command file with @ references
4. Specify target agent
5. Add clear instructions
6. Test and refine

Use /prompt-enchancer to help create complex commands.

Building Specialized Subagents

1. Identify specialized capability needed
2. Define clear scope and responsibility
3. Set appropriate permissions
4. Create focused instructions
5. Test with main agents

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Best Practices

Context Management

• Keep context files focused (50-150 lines)
• Use single-level loading (no recursive @ references)
• Load dynamically based on request analysis
• Always include core patterns

Agent Design

• Make agents deterministic (predictable behavior)
• Give clear, direct instructions (not documentation)
• Separate concerns (one agent, one responsibility)
• Use structured response formats
• Use /prompt-enchancer for complex agents

Task Management

• Break complex work into steps (15-30 minutes each)
• Use checkbox tracking for progress visibility
• Include validation criteria for each step
• Implement quality gates at key milestones

Workflow Orchestration

• Analyze before routing (complexity and domain)
• Use appropriate workflows (simple vs complex)
• Coordinate multiple agents for complex tasks
• Validate at every step (build, test, review)

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The Simple Path (Recommended)

Don't create specialized commands/agents right away. Instead:

1. Start with openagent for everything (questions and tasks)
2. Add context files for your tech stack as needed
3. Use @task-manager when features get complex (openagent delegates automatically)
4. Let subagents handle specialized work (@tester, @reviewer, @coder-agent)
5. Create specialized commands only when you have repeated workflows
6. Use /prompt-enchancer when building custom agents

Example Progression

Week 1: Use openagent for everything (questions and tasks) Week 2: Add project-specific context to project/project-context.md Week 3: Agent automatically picks up your patterns Week 4: Create a command if you have repeated workflows (use /prompt-enchancer)

How the System Improves

The system improves naturally as you:

1. Add context files - Capture your coding patterns
2. Refine agent prompts - Improve instructions based on results
3. Create project-specific commands - Automate repeated workflows
4. Build subagents - Extract specialized capabilities
5. Document in context/ - Every pattern you discover

The key principle: Start simple, extend only when you have a clear need.

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Project Structure

.opencode/
├── agent/              # AI agents
│   ├── core/
│   │   ├── openagent.md
│   │   └── opencoder.md
│   ├── meta/
│   │   └── system-builder.md
│   └── subagents/      # Specialized helpers
│       ├── reviewer.md
│       ├── tester.md
│       ├── coder-agent.md
│       ├── documentation.md
│       ├── build-agent.md
│       └── codebase-pattern-analyst.md
├── command/            # Slash commands
│   ├── commit.md
│   ├── optimize.md
│   ├── test.md
│   ├── clean.md
│   ├── context.md
│   ├── prompt-enchancer.md  # Use this to build agents!
│   └── worktrees.md
├── context/            # Coding patterns
│   ├── core/           # Essential patterns (always loaded)
│   │   └── essential-patterns.md
│   └── project/        # Your patterns (add here!)
│       └── project-context.md
├── plugin/             # Optional: Telegram notifications
└── tool/               # Optional: Gemini AI image tools

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Key Takeaways

1. Context flows one direction - Commands load immediately, Agents look up deterministically
2. Keep context focused - 50-150 lines per file, maximum 4 files per command
3. Make agents predictable - Deterministic behavior with clear instructions
4. Track everything - File-based task management with checkbox progress
5. Validate continuously - Quality gates and post-flight reviews
6. Start simple - Build core system first, add complexity gradually
7. Use /prompt-enchancer - When building custom agents with workflows

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Remember

Think of this system like a professional development team: each member has a specific role, they communicate clearly, they track their work systematically, and they validate quality at every step.

The openagent is your universal coordinator and opencoder is your senior developer. Add specialists only when needed.

When you need to build custom agents, use /prompt-enchancer to create well-structured, complex agents with proper workflows.