Edocti Training

Agentic AI Systems: Designing and Deploying Autonomous Workflows

Name: Agentic AI Systems: Designing and Deploying Autonomous Workflows
Rating: 4.9 (24 reviews)

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21 h

4.9 (24 reviews)

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The next evolution of AI: Move beyond passive chatbots and standard RAG pipelines. Learn to build Agentic AI systems capable of reasoning, planning, and executing complex tasks autonomously.

Master Tool Calling & ReAct: Teach Large Language Models how to interact with the outside world—querying databases, calling internal APIs, browsing the web, and executing code.

Multi-Agent Orchestration: Get hands-on with cutting-edge frameworks like LangGraph and AutoGen to build teams of specialized AI agents that collaborate, debate, and resolve complex workflows under a supervisor model.

Safety & Production: ~70% hands-on labs focusing on building reliable agents, implementing Human-in-the-Loop (HITL) safeguards, and preventing infinite loops or rogue execution.

Who it’s for: Senior Software Engineers, AI Researchers, and Architects looking to deploy the next generation of autonomous enterprise applications.

Skills You Will Learn

Agentic AI Architecture ReAct Prompting LLM Tool Calling LangGraph Optimization Microsoft AutoGen Multi-Agent Orchestration Human-in-the-Loop (HITL) Agent Observability

Curriculum

Foundations of Agentic AI & Tool Calling

The shift from Generation to Action: Defining an AI Agent
The ReAct framework (Reasoning + Acting): How LLMs plan their steps
Function/Tool Calling deeply explained: Schemas, execution, and returning results to the LLM
Lab: Building a single agent from scratch that queries a SQL database and a live weather API

Stateful Agents & Memory Management

Handling long-running tasks: Short-term vs. long-term memory
Semantic memory: Integrating vector databases for agent context
Handling execution errors: Self-correction and fallback prompting
Lab: Creating a data-analysis agent that writes, executes, and fixes its own Python code

Multi-Agent Orchestration (LangGraph & AutoGen)

Why Multi-Agent? Routing complexity to specialized narrow agents
Introduction to LangGraph: Treating agent workflows as cyclical graphs (StateGraph)
Microsoft AutoGen: Conversational patterns between autonomous agents
Supervisor patterns vs. Hierarchical teams vs. Peer-to-peer chat
Lab: Building a multi-agent software development team (Coder, Reviewer, Tester) with LangGraph

Production, Safety, and Human-in-the-Loop

Safety first: Sandboxing code execution and restricting API scopes
Human-in-the-Loop (HITL): Pausing agent graphs for manual approval before critical actions
Observability: Tracing agent thoughts and tool calls (LangSmith / Phoenix)
Lab: Deploying an agentic workflow as a streaming API with HITL interruption points

Optional modules

Optional — Advanced Reasoning Patterns

Tree of Thoughts (ToT) and Monte Carlo Tree Search for LLMs
Plan-and-Solve architectures for long-horizon planning
Fine-tuning open-source models specifically for tool calling

Course Day Structure

Part 1: 09:00–10:30
Break: 10:30–10:45
Part 2: 10:45–12:15
Lunch break: 12:15–13:15
Part 3: 13:15–15:15
Break: 15:15–15:30
Part 4: 15:30–17:30

Why Edocti?

The trainers: the most obvious reason. We love what we do and share the knowledge we build in day-to-day practice.
Relevant content: customized to the engineering team’s real, day-to-day needs.
Hands-on first: all our courses are practical. We don’t believe in "slide courses". Our programs are roughly 70% hands-on and 30% focused theory.
Edocti has been working on Automotive projects since 2016.
Our trainers have 11+ years of Automotive experience.
Autonomous Driving and ADAS projects for Volvo and General Motors.
V2X projects for GM and VW.
Strong collaboration with Tier-1 companies for technical training and architecture.

Who Should Attend

Senior Software Engineers, AI Developers, and Architects who are already familiar with basic LLM API integrations and want to build complex, tool-using, multi-agent AI systems.

Required Infrastructure

Laptop (Windows/macOS/Linux) with Python 3.10+ and Docker. Access to advanced reasoning models (e.g., OpenAI GPT-4o, Anthropic Claude 3, or local equivalents) via API keys will be required.