Agentic Autonomous Workflow

Learn how to create AI agents that can autonomously monitor, act, and adapt based on environment feedback.

from praisonaiagents import Agent

agent = Agent(name="planner", instructions="Act autonomously until the task is done.")
agent.start("Monitor the queue and clear stuck jobs.")

The user sets a goal; the agent loops on environment feedback until it stops or hits a completion signal.

An agent-based workflow where LLMs act autonomously within a loop, interacting with their environment and receiving feedback to refine their actions and decisions.

Quick Start

1. Install Package

   First, install the PraisonAI Agents package:

   pip install praisonaiagents

2. Set API Key

   Set your OpenAI API key as an environment variable in your terminal:

   export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"

3. Create a file

   Create a new file app.py with the basic setup:

   from praisonaiagents import Agent, AgentFlow
   from praisonaiagents import route, repeat
   
   # Create monitor agent that analyzes environment state
   monitor_agent = Agent(
       name="Monitor",
       role="Environment Monitor",
       goal="Monitor and analyze system state",
       instructions="Analyze the current state. Respond with ONLY 'normal', 'critical', or 'optimal'."
   )
   
   # Create action agents for different states
   maintenance_agent = Agent(
       name="Maintenance",
       role="System Maintainer",
       goal="Maintain normal system operations",
       instructions="Perform routine maintenance tasks. Report what was done."
   )
   
   emergency_agent = Agent(
       name="Emergency",
       role="Emergency Responder",
       goal="Handle critical situations",
       instructions="Address the critical issue immediately. Provide resolution steps."
   )
   
   optimization_agent = Agent(
       name="Optimizer",
       role="System Optimizer",
       goal="Optimize system performance",
       instructions="The system is optimal. Suggest further improvements."
   )
   
   # Create feedback agent
   feedback_agent = Agent(
       name="Feedback",
       role="Feedback Processor",
       goal="Process and learn from feedback",
       instructions="Analyze the action taken and provide feedback for improvement."
   )
   
   # Check if optimal state reached
   def is_optimal(ctx) -> bool:
       return "optimal" in ctx.previous_result.lower()
   
   # Create autonomous workflow
   workflow = AgentFlow(
       steps=[
           repeat(
               monitor_agent,  # Monitor keeps checking state
               until=is_optimal,
               max_iterations=3
           ),
           route({
               "normal": [maintenance_agent],
               "critical": [emergency_agent],
               "optimal": [optimization_agent]
           }),
           feedback_agent
       ]
   )
   
   result = workflow.start("Monitor the AI system health")
   print(f"Result: {result['output'][:500]}...")

4. Start Workflow

   Type this in your terminal to run your workflow:

   python app.py

How It Works

Choosing an Autonomy Level

Decide how much the workflow runs without a human in the loop.

Understanding Autonomous Workflow

Features

Configuration Options

# Create a monitor agent
monitor = Agent(
    name="Environment Monitor",
    role="State analyzer",
    goal="Monitor and analyze state",
    tools=[get_environment_state],  # Environment monitoring tools
      # Enable detailed logging
)

# Create an action agent
action = Agent(
    name="Action Executor",
    role="Action performer",
    goal="Execute appropriate actions",
    tools=[perform_action]  # Action execution tools
)

# Create monitoring task
monitor_task = Task(
    name="monitor_environment",
    description="Monitor environment state",
    agent=monitor,
    is_start=True,
    task_type="decision",
    condition={
        "normal": ["execute_action"],
        "critical": ["execute_action"],
        "optimal": "exit"
    }
)

# Create feedback loop task
feedback_task = Task(
    name="process_feedback",
    description="Process and adapt",
    agent=feedback_agent,
    next_tasks=["monitor_environment"],  # Create feedback loop
    context=[monitor_task, action_task]  # Access history
)

Troubleshooting

Best Practices

Next Steps

Related