AutoGPT Fundamentals Training in Marina Bay, Singapore

The AutoGPT Fundamentals Training introduces participants to autonomous AI agents using AutoGPT and similar agent frameworks.
The course focuses on understanding how agent-based systems work, how to configure and run AutoGPT-style agents, and how to apply them to task automation, research, and multi-step workflows.

This training builds a strong foundation in agent concepts, orchestration, and safe usage.

Introduction to Autonomous AI Agents

• What are AI agents?

• Difference between chatbots and agents

• Use cases for autonomous agents

• Benefits and risks of autonomy

Overview of AutoGPT & Agent Frameworks

• What is AutoGPT?

• Comparison with other agent frameworks

• Typical agent architecture

• When to use AutoGPT-style systems

Environment Setup & Installation

• Installing AutoGPT

• Environment variables and API keys

• Project structure

• Troubleshooting setup issues

Agent Configuration Basics

• Defining goals and constraints

• System prompts and role setup

• Agent personality and behavior

• Limiting scope and permissions

Task Decomposition & Planning

• How agents break down tasks

• Planning and execution loops

• Managing long-running tasks

• Monitoring agent progress

Tool Integration

• File system tools

• Web and API tools (conceptual)

• Custom tool creation (intro)

• Safe tool usage practices

Memory & Context Management

• Short-term vs long-term memory

• Storing and retrieving context

• Preventing context overload

• Memory design patterns

Human-in-the-Loop Controls

• Approval and review checkpoints

• Interrupting and correcting agents

• Supervisory workflows

• Reducing risk in autonomous runs

Prompting for Agents

• Designing agent system prompts

• Task instructions for agents

• Controlling verbosity and behavior

• Handling ambiguity

Debugging & Error Handling

• Understanding agent failures

• Log analysis

• Recovering from bad actions

• Improving agent reliability

Security, Privacy & Governance

• Preventing unsafe tool use

• Handling sensitive data

• Access controls

• Audit and compliance considerations

Performance & Cost Management

• Token usage monitoring

• Limiting runaway agents

• Resource usage control

• Cost optimization strategies

Best Practices for Safe Agent Design

• Scope limitation

• Clear success criteria

• Validation and verification steps

• Documentation of agent behavior

Hands-on Exercises

Summary and Conclusion