Workflow orchestration across agents in Agentic AI

Introduction

Workflow orchestration across agents helps different AI helpers work together smoothly. It makes sure tasks are done step-by-step without confusion.

When you want multiple AI agents to share parts of a big task.

When tasks need to happen in a certain order between agents.

When you want to automate a process that involves different AI skills.

When you want to track progress and results from many agents.

When you want to avoid agents working on the same thing twice.

Syntax

Agentic AI

orchestrator = WorkflowOrchestrator()

orchestrator.add_agent(agent1)
orchestrator.add_agent(agent2)

orchestrator.define_workflow([
    ('agent1', 'taskA'),
    ('agent2', 'taskB'),
    ('agent1', 'taskC')
])

results = orchestrator.run()

The WorkflowOrchestrator manages the order and communication.

Agents perform tasks assigned in the workflow steps.

Examples

Adds two agents: one for data and one for modeling.

Agentic AI

orchestrator.add_agent(data_agent)
orchestrator.add_agent(model_agent)

Defines a workflow where data is loaded first, then the model trains.

Agentic AI

orchestrator.define_workflow([
    ('data_agent', 'load_data'),
    ('model_agent', 'train_model')
])

Starts the workflow and collects results from all agents.

Agentic AI

results = orchestrator.run()

Sample Model

This program creates two agents: one loads data, the other trains and evaluates a model. The orchestrator runs tasks in order and prints results.

Agentic AI

class Agent:
    def __init__(self, name):
        self.name = name
    def perform(self, task, data=None):
        if task == 'load_data':
            return [1, 2, 3, 4, 5]
        elif task == 'train_model':
            data_sum = sum(data) if data else 0
            return f"Model trained with data sum {data_sum}"
        elif task == 'evaluate':
            return "Evaluation complete: accuracy 90%"
        else:
            return "Unknown task"

class WorkflowOrchestrator:
    def __init__(self):
        self.agents = {}
        self.workflow = []
    def add_agent(self, agent):
        self.agents[agent.name] = agent
    def define_workflow(self, steps):
        self.workflow = steps
    def run(self):
        data = None
        results = []
        for agent_name, task in self.workflow:
            agent = self.agents.get(agent_name)
            if agent is None:
                results.append(f"Agent {agent_name} not found")
                continue
            output = agent.perform(task, data)
            results.append(f"{agent_name} performed {task}: {output}")
            if task == 'load_data':
                data = output
        return results

# Setup agents
agent1 = Agent('data_agent')
agent2 = Agent('model_agent')

# Setup orchestrator
orchestrator = WorkflowOrchestrator()
orchestrator.add_agent(agent1)
orchestrator.add_agent(agent2)

# Define workflow
orchestrator.define_workflow([
    ('data_agent', 'load_data'),
    ('model_agent', 'train_model'),
    ('model_agent', 'evaluate')
])

# Run workflow
results = orchestrator.run()
for line in results:
    print(line)

Important Notes

Make sure agents know how to handle the tasks assigned to them.

Workflow orchestration helps avoid confusion when many agents work together.

You can extend this by adding error handling and parallel steps.

Summary

Workflow orchestration lets multiple AI agents work together step-by-step.

It helps organize tasks and share data between agents smoothly.

Using an orchestrator makes complex AI processes easier to manage.