AI Agents and Automation

Would you like to get to know the next evolutionary stage of AI – autonomous agents that can independently plan tasks, make decisions and automate complex workflows? If you would like to understand how AI agents work and how you can develop them yourself using modern frameworks such as LangChain, CrewAI or AutoGPT, then look forward to this training. From the theoretical basics of agent architectures to practical implementation with current open source tools, you will learn how to develop intelligent automation solutions for your company.

Certificate of attendance from Spirit in Projects Advanced

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Goals

Understanding the basics of AI agents: definition, architecture, autonomy
Get to know different types of agents and their areas of operation
Developing LLM-based agents using ReAct paradigm
Master current open source frameworks (LangChain, CrewAI, AutoGPT)
Implement tool use and function calling for agents
Orchestrate multi-agent systems for complex tasks
Develop practical automation solutions with AI agents
Understand safety, evaluation and deployment strategies for agents

Target Groups

AI Expert Software developer ML engineer System Architect Software Architect DevOps Engineer Automation Specialist and anyone who wants to get involved with AI agents and intelligent automation

Content

1. Basics of AI agents

What are AI agents?Definition and delimitation
From simple tools to autonomous systems
Agent vs. Model vs. Copilot
Autonomy and decision making
Perception, Action, Goal-Oriented Behavior
Agent-environment interaction
Historical development: From rule-based to LLM-based agents
Current trends: Growing importance of AI agents in enterprise applications

2. Agent architectures

Reactive Agents: Stimulus-Response
Deliberative Agents: Planning and Reasoning
Hybrid Architectures
BDI model: Beliefs, Desires, Intentions
Layered Architectures
Subsumption Architecture
Comparison and application scenarios

3. Agent scheduling algorithms

Goal-based planning
Utility-based planning
State Space Search
Forward vs backward planning
Hierarchical Task Networks (HTN)
Planning algorithms in practice

4. Reinforcement learning for agents

Basics of Reinforcement Learning
Markov Decision Processes (MDP)
Q-Learning and Deep Q-Networks (DQN)
Policy gradient methods
Multi Armed Bandits
RL for autonomous agents

5. LLM based agents

Revolution through Large Language Models
LLMs as Reasoning Engines
ReAct paradigm: Reasoning + Acting
Observation, Thought, Action Loop
Chain-of-thought for agents
Tree-of-Thought for complex decisions
Self-reflection and self-correction

6. Tool Use and Function Calling

Concept of tool use in LLMs
Function Calling: OpenAI, Anthropic, Google approaches
Tool description and schemas
Multi-tool orchestration
Connect external APIs: Web Search, Calculator, Database
Error handling and retry strategies
Practical Exercise: Multi-Tool Agent (Google Colab)

7. Memory and context management

Short-term vs. long-term memory
Conversation history management
Memory Types: Buffer, Summary, Entity, Knowledge Graph
Vector databases as long-term memory
Context window optimization
Practical exercise: Agent with memory system

8. LangChain for agents

Overview of LangChain Framework
Agents in LangChain: Zero-shot, Conversational, ReAct
Tools and toolkits
Chains vs Agents
AgentExecutor and Agent Types
Custom tool development
LangSmith for agent debugging
Practical exercise: ReAct agent with LangChain (Google Colab)

9. LlamaIndex for Data Agents

LlamaIndex basics
Data Agents for RAG workflows
Query engines and data connectors
Multi-Document Agents
Integration with vector databases
Practical exercise: RAG agent with LlamaIndex

10. Autonomous Agents: AutoGPT and BabyAGI

Autonomous task execution concept
AutoGPT: Architecture and functionality
BabyAGI: Task-driven Autonomous Agent
Task decomposition and prioritization
Iterative task planning and self-critique
Limitations and challenges
Practical exercise: Autonomous Research Agent (Google Colab)

11. Multi-agent systems

Why multi-agent systems?
Agent communication and coordination
Role-based agent design
Collaborative vs Competitive Agents
Consensus and negotiation
Task distribution and load balancing

12. CrewAI for Multi-Agent Orchestration

Overview of CrewAI Framework
Crews, agents, tasks, tools
Role-based agent definition
Process Types: Sequential, Hierarchical, Consensual
Agent Delegation and Collaboration
Output handling and result aggregation
Practical exercise: Multi-agent system with CrewAI (Google Colab)

13. Cost optimization

Minimize token usage among agents
Caching strategies
Model selection: High end models vs. low end vs. open source
Routing: Simple tasks to cheaper models
Batching and parallelization
Cost monitoring and budget alerts

Certification

For this training you will receive a certificate of participation from Spirit in Projects.