Agent Members & Coordination
Organize specialized AI agents as team members with defined roles and capabilities
What Are Agent Members?
Agent Members are AI agents configured as organization members with custom roles and system prompts. Each agent is launched via CLI and connects to your Agiflow backend for task assignment and session management.
Unlike single-agent setups where you manually manage context and switching between tasks, agent members provide organizational structure with role-based task assignment and session continuity.
The Problem: Managing Multiple AI Agent Contexts
When working with AI agents on complex projects, you face several challenges:
Manual Context Switching
You manually prompt each agent for their role: "Act as a backend developer..." or "Review this code for security...". This overhead adds up across tasks.
No Specialized Model Selection
Different tasks need different AI capabilities. Code review benefits from reasoning models like GPT-5, while rapid prototyping works better with Claude. Manual model switching is tedious.
Lost Session Context
When switching between tasks or models, you lose session continuity. No clear tracking of which agent worked on which task or what was accomplished.
How Agent Members Solve This
1. Define Specialized Agents Once
Create agent members in your organization with custom roles and system prompts. These configurations are stored and reused across all tasks.
Example Agent Configurations
Role: Backend API Developer
Prompt: "You are a Node.js backend developer specializing in Hono.js APIs. Focus on OpenAPI specs, proper error handling, and Drizzle ORM patterns..."
Role: React SPA Developer
Prompt: "You build React SPAs with TanStack Router and Query. Use @agimonai/web-ui components, follow accessibility standards..."
Role: Code Review & Architecture
Prompt: "Review code for security, performance, and architectural consistency. Check against project design patterns and coding standards..."
2. Launch Agents with Session Management
Use the agent-cli to launch agents with specific configurations. Each agent gets a session ID and alias for easy identification and management.
3. Dynamic Model Routing
Switch between AI models without restarting sessions. Route requests to different providers (Claude, GPT-5, GPT-5 Codex) with configurable reasoning effort levels.
Available reasoning levels: high, medium, low, minimal
4. Backend Integration for Task Assignment
When connected to Agiflow backend (via agent-cli connect), agents automatically receive task assignments, work unit context, and MCP tool configurations.
Agent CLI Workflow
Step 1: Connect Daemon (Optional)
For backend-connected mode, start the Agiflow daemon to receive task assignments and sync session state.
Step 2: Configure Agent Members (Dashboard)
In your Agiflow dashboard, create agent members with roles and custom prompts. These configurations are pulled by the CLI when launching agents.
Step 3: Launch Agents for Tasks
Launch agents with session IDs matching your tasks. The agent automatically applies the member's custom prompt and configuration.
Step 4: Dynamic Routing (Optional)
Change AI models for running sessions without restart. Useful when a task needs different reasoning capabilities mid-execution.
Step 5: Session Tracking
All agent activity is logged with session IDs and aliases. View logs in the dashboard to track which agent worked on which task and what was accomplished.
Key Capabilities
Persistent Prompts
Define agent roles once in dashboard. CLI automatically applies the custom prompt when launching with --agent-session-id.
Model Flexibility
Route to Claude (default), GPT-5, or GPT-5 Codex with configurable reasoning effort (high, medium, low, minimal). Switch models dynamically via agent-cli router.
Standalone Mode
Run agents locally without backend connection (--standalone). Useful for testing agent configurations or working offline.
Session Aliases
Use human-readable aliases (--alias "backend-oauth") for easy session identification instead of UUIDs. Aliases appear in logs and routing menus.
Docker Support
Run agents in Docker containers (--docker) for isolated execution environments. Useful for testing or sandboxed tasks.
Related Documentation
Build your specialized AI agent team
Configure agent members with domain expertise and start delegating work systematically.