Course
Course Overview
This one‑day technical program provides a comprehensive and practical introduction to modern AI agents and agentic systems for technology professionals. Designed for learners who already understand the fundamentals of AI, the course explores how agents reason, plan, use tools, retrieve information, maintain memory and execute complex multi‑step workflows. Participants will examine agent architectures, orchestration patterns, evaluation frameworks, safety mechanisms and enterprise deployment considerations that are driving AI adoption in 2026. Through hands-on exercises and implementation-focused discussions, attendees will learn how to design, evaluate and operationalize AI agents in real-world business environments.
What you will learn
Understand how AI agents operate, reason, plan and execute tasks autonomously.
Build multi‑step agent workflows capable of handling complex business processes.
Design agent architectures using memory, retrieval, tools and workflow orchestration concepts.
Evaluate agent performance, reliability and safety using structured frameworks.
Implement guardrails, governance controls and human oversight mechanisms.
Understand enterprise deployment patterns, scalability considerations and operational risks.
Design agent-based solutions for real business and technology use cases.
Who This Course Is For
This course is designed for AI engineers, software developers, solution architects, data scientists, ML engineers, technical consultants, DevOps professionals, IT managers and technology leaders who already possess a basic understanding of AI and want to deepen their knowledge of agentic systems and enterprise AI automation.
Before You Start
Participants should be familiar with AI fundamentals, machine learning concepts and basic software development principles. Understanding APIs, automation workflows, data processing concepts and system design will help participants maximize the value of the technical labs and architecture discussions.
Course Overview
Duration: 1 Day
Course ID : NUV-AIAGNT1-101
Case-based learning
Expert-led sessions & Interactive workshops
Practical strategy exercises
Start your AI journey today.
Module 1 : AI Agents Fundamentals
● From AI Systems to Agents : Understand how agentic AI differs from traditional AI applications and conversational systems. ● Core Agent Components : Goals, planning, reasoning, execution, memory and feedback loops. ● Types of Agents : Reactive agents, planning agents, collaborative agents, autonomous agents and multi‑agent systems. ● Agentic AI in 2026 : Current industry trends, enterprise adoption patterns and emerging capabilities.
Module 2 : Agent Architectures, Reasoning & Planning Systems
● Reasoning Frameworks : How agents analyze objectives, break down tasks and make decisions. ● Planning Mechanisms : Task decomposition, sequencing and execution strategies. ● Memory Systems : Working memory, long‑term memory and contextual knowledge management. ● Agent Lifecycle : Observe → Reason → Plan → Execute → Reflect loops.
Module 3 : Retrieval, Knowledge Access & Context Management
● Retrieval-Augmented Agent Design : Enhancing agent accuracy using external knowledge sources. ● Context Management : Handling long workflows, large documents and dynamic information. ● Knowledge Grounding : Reducing hallucinations and improving factual reliability. ● Hands‑On Lab : Design a retrieval-enabled agent workflow for a business scenario.
Module 4 : Tool Use, Function Execution & Workflow Orchestration
● Agent Tool Usage : How agents invoke systems, services and business functions. ● Workflow Orchestration : Coordinating tasks, dependencies and execution paths. ● Agent Decision Logic : Determining when and how actions should be executed. ● Hands‑On Lab : Build a multi-step workflow involving planning, execution and validation stages.
Module 5 : Multi‑Agent Systems & Collaborative Intelligence
● Specialized Agent Roles : Research agents, planner agents, reviewer agents and executor agents. ● Agent Collaboration Models : Coordination, delegation and communication between agents. ● Workflow Distribution : Managing large processes through multiple specialized agents. ● Practical Exercise : Design a multi-agent architecture for a business process.
Module 6 : Evaluation, Reliability & Agent Observability
● Agent Performance Measurement : Accuracy, completion rate, latency and quality metrics. ● Failure Modes : Hallucinations, loops, missed objectives and tool misuse. ● Observability & Monitoring : Tracking agent behavior and execution paths. ● Hands‑On Lab : Evaluate and improve an agent workflow using structured assessment criteria.
Module 7 : Security, Governance & Enterprise Controls
● Agent Security Risks : Prompt injection, data exposure, unauthorized actions and escalation risks. ● Human‑in‑the‑Loop Controls : Approval workflows and intervention mechanisms. ● Governance Frameworks : Accountability, auditability and risk management practices. ● Enterprise Compliance : Data protection, monitoring and deployment controls.
Module 8 : Deploying & Scaling Agentic Systems
● Enterprise Integration Patterns : Embedding agents within organizational workflows and systems. ● Scalability Considerations : Performance, reliability and operational efficiency. ● Production Readiness : Testing, validation and rollout strategies. ● Architecture Best Practices : Designing maintainable and resilient agent solutions.
Module 9 : Capstone - Design an Enterprise AI Agent Solution
● Use Case Definition : Select a high-value agent opportunity. ● Architecture Design : Create an end-to-end agent workflow and system architecture. ● Governance & Safety Layer : Apply security, monitoring and approval mechanisms. ● Final Presentation : Deliver a complete enterprise-ready AI agent solution blueprint.
You will learn how to use various trending and in‑demand AI agent technologies and capabilities in practical ways by :
Deliverables
The Final Certification Exam will be conducted on the Certify Assessment (UK) and successful candidates will be awarded officially recognized credentials.