These examples demonstrate the core features of Manager Agent Gym with crystal-clear narratives.
The Manager Agent Gym is a research platform for autonomous workflow management. These examples show you how AI managers can orchestrate complex workflows involving both human and AI collaborators.
Goal: See the complete workflow execution cycle in action
What it demonstrates:
- ✅ Creating workflows with the
WorkflowBuilder - ✅ Setting up manager agents with preferences
- ✅ Running execution with the
WorkflowExecutionEngine - ✅ Observing manager decision-making in real-time
Run it:
cd manager_agent_gym
python examples/getting_started/hello_manager_agent.pyWhat you'll see: A manager agent taking charge of a simple research workflow, making decisions about task assignment and coordination.
AI agents that observe workflow state and make strategic decisions:
- Assign tasks to agents
- Create new tasks when needed
- Monitor progress and adapt to changes
- Balance multiple preferences (quality, time, cost, etc.)
Collections of interconnected tasks with:
- Task dependencies and scheduling
- Resource requirements and outputs
- Regulatory constraints
- Mixed human and AI agent teams
The manager's optimization criteria:
- Quality: Focus on excellent deliverables
- Time: Minimize delays and optimize timelines
- Cost: Resource efficiency and budget consciousness
- Oversight: Balance supervision needs
The simulation environment that:
- Runs discrete timesteps
- Manages task execution asynchronously
- Provides observations to managers
- Tracks comprehensive metrics
After running these basic examples, explore:
- Research Examples: See how the platform tackles cutting-edge research challenges
- Advanced Features: Preference dynamics, regret analysis, governance constraints
- Custom Implementations: Build your own manager agents and evaluation metrics
The goal is to make autonomous workflow management research both accessible and rigorous! 🎊