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Learning by doing
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Trainers with practical experience
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Classroom training
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Detailed course material
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Clear content description
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Tailormade content possible
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Training that proceeds
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Small groups
The course AutoGPT Workflow Automation from SpiralTrain teaches you how to build autonomous AI agents using AutoGPT and similar frameworks. You will learn how to design self-directed workflows, integrate tools and APIs, manage memory and context, and create intelligent agents that can accomplish complex tasks with minimal human intervention.
The course AutoGPT Workflow Automation begins with an introduction to autonomous AI agents and AutoGPT. Agent frameworks comparison, installation, configuration, goal setting, memory systems, and practical use cases are explored.
This module covers the agent loop design including goal decomposition, task planning, reasoning processes, self-reflection, critique mechanisms, decision making, action selection, and feedback loops for continuous improvement.
Here participants learn to work with built-in tools and develop custom tools. Topics include API integrations, web browsing, file operations, code execution, search capabilities, database access, and plugin architecture.
Memory systems are addressed including short-term and long-term memory, vector databases, retrieval strategies, context window management, conversation history, knowledge storage, embeddings, and state persistence for autonomous agents.
This part focuses on advanced features including multi-agent systems, agent collaboration, human-in-the-loop workflows, constraint management, cost optimization, error handling, guardrails, safety mechanisms, and workflow orchestration.
The course concludes with production considerations including deployment strategies, environment configuration, API management, monitoring, error recovery, scaling, security best practices, cost management, and testing autonomous agents.
This course is intended for AI developers, automation engineers, and data scientists who want to build autonomous AI agents using AutoGPT.
Participants should have a good understanding of Python Programming en AI and LLM concepts. Familiarity with APIs and prompt engineering is beneficial.
The training combines theoretical instruction with hands-on labs guided by a trainer. Participants build several autonomous agents throughout the course.
After successful completion, participants receive a certificate of participation in AutoGPT Workflow Automation.
Module 1: Introduction AutoGPT |
Module 2: Agent Architecture |
Module 3: Tools and Plugins |
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AutoGPT Overview Autonomous Agents Concepts AutoGPT vs ChatGPT Agent Frameworks Comparison Installation and Setup Configuration Basics Agent Goals Memory Systems Use Cases Best Practices |
Agent Loop Design Goal Decomposition Task Planning Reasoning Processes Self-Reflection Mechanisms Critique and Refinement Decision Making Action Selection Feedback Loops Agent Personas |
Built-in Tools Custom Tool Development API Integrations Web Browsing File Operations Code Execution Search Capabilities Database Access Tool Chaining Plugin Architecture |
Module 4: Memory Management |
Module 5: Advanced Capabilities |
Module 6: Production Deployment |
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Short-Term Memory Long-Term Memory Vector Databases Memory Retrieval Context Window Management Conversation History Knowledge Storage Embedding Strategies Memory Optimization State Persistence |
Multi-Agent Systems Agent Collaboration Human-in-the-Loop Constraint Management Cost Optimization Error Handling Guardrails Implementation Safety Mechanisms Performance Tuning Workflow Orchestration |
Deployment Strategies Environment Configuration API Key Management Monitoring and Logging Error Recovery Scaling Considerations Security Best Practices Cost Management Testing Autonomous Agents Production Checklist |
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