Pipelines

Building Complex Automation Pipelines

One of Peeps AI's core strengths lies in its ability to construct and manage complex automation pipelines. These pipelines enable you to coordinate tasks, agents, and workflows in sophisticated, modular, and highly scalable systems. This section provides a detailed guide on how to design, implement, and optimize automation pipelines using Peeps AI’s Workchains and Peeps orchestration.

By combining event-driven workflows, inter-agent collaboration, and precise task management, you can automate complex real-world processes, such as data processing, decision-making, and system integration, with unparalleled efficiency and control.

---

Key Concepts in Automation Pipelines

❖ Workchains

Workchains are the backbone of Peeps AI's automation capabilities. They are event-driven workflows that allow for:

• Conditional branching: Decisions based on dynamic inputs or outputs.

• Parallel execution: Running multiple tasks simultaneously.

• Hierarchical delegation: Assigning sub-tasks to specialized agents or groups.

• State management: Maintaining context across tasks.

❖ Agents and Collaboration

Each agent in Peeps AI operates autonomously within its role but can collaborate with other agents in a pipeline. This collaboration includes:

• Delegating tasks dynamically.

• Sharing intermediate results.

• Inquiring and validating data across roles.

❖ Integration with External Systems

Peeps AI pipelines can integrate with external APIs, databases, or custom tools using the modular tool interface. This makes it easy to include third-party systems in the automation workflow.

---

Designing an Automation Pipeline

Step 1: Define the Workflow

Start by breaking down your workflow into discrete tasks. Identify:

• The sequence of operations.

• Points of decision-making or branching.

• Tasks that can run in parallel.

For example, an e-commerce fraud detection pipeline may involve:

❖ Data ingestion from transactions.

❖ Risk scoring using AI models.

❖ Manual review for borderline cases.

❖ Automated notifications based on the outcome.

Step 2: Define Agents

Assign roles and responsibilities to each agent based on the tasks in the workflow. For example:

• Data Analyst: Processes raw transaction data.

• Risk Assessor: Calculates risk scores.

• Reviewer: Validates flagged transactions.

Define these agents in agents.yaml:

data_analyst:
  role: "Data Analyst"
  goal: "Process transaction data and extract features."
  
risk_assessor:
  role: "Risk Assessor"
  goal: "Analyze transaction data to generate risk scores."
  
reviewer:
  role: "Manual Reviewer"
  goal: "Validate high-risk transactions."

Step 3: Configure Tasks

Set up tasks in tasks.yaml with clear descriptions, inputs, and expected outputs:

process_data_task:
  description: "Process and clean transaction data for analysis."
  expected_output: "A structured dataset ready for scoring."
  agent: data_analyst

risk_scoring_task:
  description: "Calculate risk scores based on transaction features."
  expected_output: "A list of transactions with risk scores."
  agent: risk_assessor

manual_review_task:
  description: "Manually review flagged high-risk transactions."
  expected_output: "Final decision on transactions."
  agent: reviewer

Step 4: Design the Workchain

Using the group.py file, define the automation pipeline as a Workchain. Here is an example:

from peepsai import Peeps, Agent, Task, Process
from peepsai.project import PeepsBase, task, group

class FraudDetectionWorkflow(PeepsBase):
    """Automation pipeline for fraud detection in e-commerce."""
    
    @task
    def process_data(self) -> Task:
        return Task(config=self.tasks_config['process_data_task'])

    @task
    def risk_scoring(self) -> Task:
        return Task(config=self.tasks_config['risk_scoring_task'])

    @task
    def manual_review(self) -> Task:
        return Task(config=self.tasks_config['manual_review_task'])

    @group
    def fraud_pipeline(self) -> Peeps:
        """Define the full pipeline."""
        return Peeps(
            agents=self.agents,
            tasks=self.tasks,
            process=Process.sequential,  # Tasks execute in order
            verbose=True
        )

---

Advanced Pipeline Features

❖ Conditional Branching

Make pipelines dynamic by including conditional branching. For example, only send high-risk transactions to manual review:

from peepsai.flow.flow import router

@router(risk_scoring)
def decide_next_step(self):
    if self.state['risk_score'] > 0.8:
        return "manual_review"
    return "complete"

❖ Parallel Execution

Run tasks in parallel to save time. For example, data processing and risk scoring can occur simultaneously:

fraud_pipeline = Peeps(
    tasks=[process_data, risk_scoring],
    process=Process.parallel
)

❖ Sub-Groups and Delegation

Complex workflows can be broken into smaller sub-groups managed by dedicated agents:

@group
def data_processing_group(self) -> Peeps:
    return Peeps(
        agents=[self.agents['data_analyst']],
        tasks=[self.tasks['process_data']],
        process=Process.sequential
    )

❖ Real-Time Monitoring

Use Peeps AI's verbose logging and state management features to track pipeline progress and debug issues effectively.

---

Optimization Tips

❖ Minimize Latency: Use parallel processing for independent tasks.

❖ Error Handling: Add robust error handling for critical tasks.

❖ State Management: Track pipeline states using structured objects.

❖ Scalability: Modularize pipelines for easier maintenance and scaling.