Craft autonomous agentic AI systems

AI workflows can follow predefined rules, but agentic systems go further by reasoning about goals, selecting tools, and deciding what to do next.

On this ExpertTrack you’ll learn how to design and evaluate autonomous AI systems built around single and multiple agents.

Start with the foundations of agent architecture, including the agent loop, tool use, and different forms of memory.

You’ll compare reactive, goal-directed, and memory-augmented agents and explore how system prompts and structured outputs shape agent behaviour.

Build single-agent systems

Design agents that can interpret instructions, call tools, maintain useful context, and return structured results.

You’ll explore tool configuration and methods for evaluating whether an agent has completed its assigned task successfully.

Coordinate multi-agent workflows

Progress to orchestrator and worker architectures, task decomposition, role assignment, and context passing between agents.

Learn how sequential and parallel execution patterns support more complex workflows while shared memory and state management help agents coordinate.

Evaluate AI agent performance

Explore how to assess task completion, handoff quality, looping behaviour, and common failure modes across an agent system.

By the end, you’ll understand how to design, coordinate, and evaluate agentic AI systems for complex automated tasks.

Syllabus

Week 1
Architecting and Building Tool-Enabled AI Agents
- Stepping into Agentic AI
  Discover how agentic AI differs from traditional AI, examine core agent concepts and consider where autonomous systems can add value in real-world work.
- Building Your First AI Agent with n8n
  Build your first AI agent in n8n by connecting models, prompts and workflow components to create a practical content-generation agent.
- Equipping Agents with Tools in Flowise
  Use Flowise to connect models, chains and external tools, then build and test agents that can reason, process information and take useful actions.
Week 2
Supercharging AI Agents with Memory, RAG, and Human Review
- Adding Memory and Context to AI Agents
  Add memory and context to AI agents so they can maintain conversations, retain useful information and support more personalised workflows.
- Grounding Agents with RAG
  Build RAG-powered agents that retrieve relevant information from documents, use vector stores and produce responses grounded in trusted sources.
- Orchestrating Content Review, Approval, and Delivery
  Automate content workflows by combining AI generation, approval stages, event triggers and scheduled delivery.
Week 3
Engineering Multi-Agent Orchestration and Coordination
- Designing Multi-Agent Architectures
  Design multi-agent systems by assigning roles, decomposing tasks and combining agents through supervisory, sequential and parallel workflow patterns.
- Coordinating Multi-Agent Communication and Shared State
  Coordinate multiple agents through shared memory, communication patterns and A2A interactions while examining how agents exchange information and resolve tasks.
- Evaluating and Troubleshooting Multi-Agent Systems
  Evaluate multi-agent workflows, diagnose coordination failures and apply fallback strategies to improve reliability, efficiency and error handling.

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Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

Describe the core concepts, agent types, and business use cases of agentic AI.
Practice building tool-enabled AI agents and visual agent workflows using n8n and Flowise.
Demonstrate how memory, context, state, and RAG improve agent behaviour and knowledge grounding.
Design multi-step agentic workflows that include review, scoring, approval, publishing, webhooks, and event triggers.
Investigate multi-agent coordination patterns, shared memory, A2A communication, handoffs, troubleshooting, and fallback handling.

Who is the course for?

This ExpertTrack is for automation engineers, AI developers, solutions architects, and technology professionals with workflow experience who want to design single- and multi-agent systems that reason, use tools, and act autonomously.

What software or tools do you need?

No prior coding experience is required. You will need a suitable computer, a stable internet connection, and access to the no-code AI agent platforms used in the practical activities. This course uses tools such as n8n, Google API credentials, and Flowise to build, test, and connect AI agents.

Who will you learn with?

Edureka Instructors

Who developed the course?

Edureka

Edureka is a global online learning platform offering industry-focused training in emerging technologies such as Cloud, AI, GenAI, Data Science, DevOps, and Cybersecurity, helping professionals upskill and advance their careers.

About this ExpertTrack

Build practical generative AI and workflow automation skills, from prompt engineering to agentic AI and enterprise deployment.

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