Implementing AI Agents and Autonomous Workflows

Build smart assistants that take initiative and complete tasks.

Pillar

Technology – Platforms, Tools, Infrastructure & Productivity

Overview

This course guides participants through designing, developing, and deploying AI agents and autonomous workflows that can independently perform complex tasks. Focusing on the practical implementation of intelligent assistants, attendees will learn how to enable AI systems to make decisions, interact with other applications, and automate end-to-end business processes.

Learning Objectives

Participants will be able to:

• Understand the architecture and components of AI agents and autonomous workflows

• Design AI agents capable of proactive decision-making and task execution

• Integrate AI agents with existing business systems and APIs

• Develop workflows that combine AI with human oversight for safety and efficiency

• Monitor, troubleshoot, and optimize autonomous AI workflows in production

Target Audience

• AI developers and solution architects

• Process automation specialists

• Technical leads and innovation managers

• DevOps and MLOps engineers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures with real-world examples

• Hands-on labs creating AI agents and workflow automations

• Group discussions on governance and ethical considerations

• Case study reviews and problem-solving exercises

Materials Provided

• Sample AI agent templates and code repositories

• Workflow design blueprints and integration guides

• Access to development environments and API tools

• Course slides and reference materials

Outcomes

• Build autonomous AI agents that handle business tasks independently

• Integrate AI agents into complex workflows with minimal human intervention

• Ensure AI workflows are transparent, controllable, and compliant

• Optimize agent performance and reliability through continuous monitoring

Outline / Content

Day 1: Introduction to AI Agents and Autonomous Workflows

• Concepts and types of AI agents

• Use cases for autonomous workflows in business

• Architecture overview: components and integration points

Day 2: Designing and Developing AI Agents

• Building decision-making and task execution logic

• Connecting agents to APIs and enterprise systems

• Handling errors and exceptions in autonomous agents

Day 3: Workflow Automation and Human-in-the-Loop Models

• Combining AI automation with human oversight

• Designing feedback loops and safety mechanisms

• Tools for workflow orchestration and monitoring

Day 4: Deployment, Monitoring, and Optimization

• Deploying agents in production environments

• Monitoring performance and troubleshooting issues

• Continuous improvement and scaling strategies