Agentic AI for Developers

Why Choose Garranto Academy for Your Agentic AI for Developers Program?

Garranto Academy offers hands-on, developer-focused AI training designed to move beyond basic prompt engineering into real-world agentic system development. This program is built for engineers who want to design AI systems that can plan, reason, act, and collaborate autonomously.

Through practical labs and real-world scenarios, participants gain deep experience in building reliable, observable, and production-ready agentic workflows using modern AI orchestration patterns.

Course Overview

The Agentic AI for Developers program is a comprehensive 5-day hands-on training designed for developers and engineers who want to build autonomous AI systems.

The course focuses on designing and implementing agentic workflows using LangGraph, Python-based agent-to-agent communication, and workflow orchestration tools. Participants learn how to create AI agents that can coordinate tasks, manage state, communicate with other agents, and safely interact with external systems.

By the end of the program, participants will deliver a fully working agentic AI system integrating multiple agents, tools, APIs, and automation workflows.

Course Objectives

• Understand the fundamentals of agentic AI systems and autonomous workflows

• Design and implement agent-based architectures using modern AI orchestration frameworks

• Build reliable agent-to-agent communication using Python services

• Orchestrate AI workflows across systems using automation tools

• Apply observability, validation, and safety controls to agentic systems

• Deliver a complete end-to-end agentic AI solution

Prerequisites

• Programming Background: Proficiency in Python or similar programming language

• AI Fundamentals: Basic understanding of LLMs and generative AI concepts

• API Knowledge: Familiarity with REST APIs and JSON-based communication

• Professional Experience: Suitable for developers, engineers, and technical architects

Course Outline

Module 1: Foundations of Agentic AI

Objective:

Understand the core concepts and building blocks of agentic AI systems.

Key Topics:

• What is agentic AI and how it differs from traditional AI systems

• Agents, tools, memory, state, and control flow

• Planning, reasoning, and action loops

• Single-agent vs multi-agent architectures

Practical Studio:

Build a basic agent capable of reasoning and tool execution.

Outcome:

Clear understanding of agentic AI concepts and architecture.

Module 2: Building Agent Workflows with LangGraph

Objective:

Design structured, multi-step agent workflows using LangGraph.

Key Topics:

• LangGraph fundamentals: nodes, edges, and state

• Tool calling and function schemas

• Conditional routing, retries, and loops

• Memory and context handling

• Logging, tracing, and debugging agent flows

Practical Studio:

Create a LangGraph-based agent workflow with decision logic.

Outcome:

Working agent workflow with observability and control.

Module 3: Agent-to-Agent Communication with Python

Objective:

Enable collaboration between multiple agents using Python services.

Key Topics:

• Agent-to-agent communication patterns

• Message schemas and validation

• Building agent APIs using FastAPI

• Asynchronous execution and error handling

• Correlation IDs and structured logging

Practical Studio:

Implement two collaborating agents exchanging tasks and results.

Outcome:

Functional multi-agent communication system.

Module 4: Workflow Orchestration and Automation

Objective:

Orchestrate agent workflows across systems using automation tools.

Key Topics:

• Workflow orchestration concepts

• Trigger-based execution and scheduling

• Integrating agents with external APIs and services

• Error handling and fallback strategies

Practical Studio:

Build an automated workflow that coordinates multiple agents.

Outcome:

End-to-end automated agent workflow.

Module 5: End-to-End Agentic System Integration

Objective:

Integrate agents, workflows, and services into a complete system.

Key Topics:

• End-to-end architecture patterns

• Data contracts and schema versioning

• Deployment considerations

• Environment configuration and secrets management

Practical Studio:

Integrate all components into a single running system.

Outcome:

Fully integrated agentic AI system.

Module 6: Safety, Governance, and Reliability

Objective:

Apply safety, governance, and reliability practices to agentic AI systems.

Key Topics:

• Input validation and output filtering

• Guardrails and policy enforcement

• Rate limiting and access control

• Testing and monitoring strategies

Practical Studio:

Harden the agentic system with validation and safety controls.

Outcome:

Production-ready agentic system with governance controls.

Module 7: Capstone Project – Agentic AI System

Objective:

Build and present a complete agentic AI solution.

Key Topics:

• Multi-agent coordination and orchestration

• Observability and performance tracking

• AI-generated documentation and demos

Capstone Deliverables:

• Working agentic AI system

• Source code and documentation

• Demonstration and walkthrough

Outcome:

Complete agentic AI solution ready for real-world use.

Course Outcomes

Upon successful completion of this course, participants will be able to:

• Design and implement agentic AI systems

• Build multi-agent workflows with autonomous collaboration

• Orchestrate AI agents across services and tools

• Apply safety, observability, and governance best practices

• Deliver a production-ready agentic AI application

Target Audience

This program is ideal for:

• Python Developers and Software Engineers

• AI and Machine Learning Engineers

• Backend and API Developers

• Automation and Workflow Engineers

• Solution Architects and Technical Leads

• Developers transitioning into advanced AI systems