Building AI Agents

The course Building AI Agents from SpiralTrain teaches you to build intelligent AI agents that reason, act, and collaborate autonomously.

Intro AI Agents

The course begins with a look at what AI agents are, how they differ from chatbots, and their core components like autonomy and decision-making. It covers LLMs as reasoning tools, agent frameworks like LangChain, memory usage, tools, and common challenges.

LangChain Fundamentals

This module explores the LangChain architecture. Topics include chains vs agents, templates, memory modules, document loaders, streaming output, tool integration, and how to debug agents using LangSmith.

Building First Agent

Learn to create your first AI agent by choosing a model, setting goals, integrating tools, managing memory, and writing effective prompts. Also covered: error handling, multi-step tasks, personality, and monitoring output.

Multi-Agent Systems

Participants explore collaboration between agents using frameworks like CrewAI and Autogen. The module covers roles, messaging, goal refinement, workflow monitoring, task decomposition, and evaluation techniques.

Agent Use Cases

Dive into real-world applications like coding assistants, customer service bots, finance and research agents, enterprise tasks, web deployment, Slack integration, and analytics to assess impact and improve performance.

Future of AI Agents

The course ends with trends in AI agents—self-improvement, real-time sensing, ethical concerns, memory growth, regulation, and human-agent teamwork. It also looks at future marketplaces and safety controls.

Audience Course Building AI Agents

The course Building AI Agents is intended for software developers, data scientists, and AI practitioners who want to learn how to design autonomous agents using LLM's.

Prerequisites Building AI Agents Course

To participate in this course, a basic understanding of Python programming and machine learning concepts is required. Familiarity with APIs and prompt engineering is useful.

Realization Training Building AI Agents

The course is conducted under the guidance of the trainer, combining theory with hands-on exercises. Real-world examples and practical case studies are used throughout the training.

Building AI Agents Certificate

After successfully completing the course, participants will receive a certificate of participation in Building AI Agents.

Modules

Module 1: Intro AI Agents

• What is an AI agent?
• Core Components
• Autonomy and Decision Making
• Agents vs Chatbots
• Key Frameworks (LangChain, Auto-GPT)
• LLMs as Reasoning Engines
• Tools and APIs
• Role of Memory
• Agent Use Cases
• Challenges and Risks

Module 2: LangChain Fundamentals

• LangChain Architecture
• Chains and Agents
• Prompts and Templates
• Tool Integrations
• Document Loaders
• Memory Modules
• Output Parsers
• Streaming Output
• Agent executors
• LangSmith for Debugging

Module 3: Building First Agent

• Choosing an LLM
• Defining Goals and Actions
• Using Tools (search, calculator)
• Writing Prompts for Agents
• Handling Errors and Retries
• Adding Personality
• Managing State and Memory
• Multi-step Tasks
• Logging and Monitoring
• Sandbox Environments

Module 4: Multi-Agent Systems

• Collaboration Between Agents
• CrewAI and Autogen Overview
• Roles and Responsibilities
• Message Passing between Agents
• Task Decomposition
• Goal Refinement
• Monitoring Progress
• Conflict Resolution
• Complex Workflows
• Evaluation Strategies

Module 5: Agent Use Cases

• Coding Assistant
• Research Assistant
• Personal Finance Agent
• Enterprise Task Agent
• AI Bots for Customer Support
• Integrating with Slack/Teams
• Running on the Web
• Continuous Learning Agents
• Logging and Analytics
• Measuring Impact

Module 6: Future of AI Agents

• Self-Improving Agents
• Memory Evolution
• Real-time Environment Sensing
• AI Decision Making
• Simulated Personalities
• Ethics and Control
• Guardrails and Safety
• Regulation Implications
• Agent Marketplaces
• Agent + Human Collaboration