Agentic AI Design & Development Course in Dubai | 4-Day Online Course

Upcoming sessions

Curriculum

1

Agent versus chatbot distinctions

2

Agent versus RPA comparison

3

Levels of autonomy

4

Agentic capabilities and limits

5

Use-case selection criteria

6

Bounded business task framing

AI Integration

Analyze use-case descriptions with AI to classify suitable agentic opportunities
Support requirement framing by generating bounded task definitions and risk assumptions

Activities/Case Study

Exercise: explore agent use cases.
Case study: chatbot versus agent.
Simulation: identify autonomy levels.

1

Goals, environment, inputs, outputs

2

State and failure modes

3

ReAct reasoning loops

4

Planning, reflection, routing

5

Orchestrator worker patterns

6

Workflow versus autonomous agents

AI Integration

Analyze task flows with AI to propose suitable agent architecture patterns
Support design reviews by generating failure modes and control checkpoints

Activities/Case Study

Hands-on: map agent boundaries.
Case study: architecture trade-offs.
Simulation: choose design patterns.

1

System prompt design

2

Prompt and context strategy

3

Structured outputs design

4

Model selection criteria

5

Latency and cost trade-offs

6

Reliable instruction handling

AI Integration

Analyze prompt variants with AI to improve consistency and output structure
Support model selection using AI-generated comparisons of cost and capability

Activities/Case Study

Hands-on: refine system prompts.
Case study: output reliability.
Simulation: compare model choices.

1

Custom tool design

2

Function calling workflows

3

API integration patterns

4

Input validation controls

5

Least-privilege tool access

6

Tool misuse prevention

AI Integration

Analyze API specifications with AI to draft tool schemas and parameters
Support secure tool design by flagging excessive permissions and unsafe inputs

Activities/Case Study

Hands-on: build custom tools.
Case study: API integration.
Simulation: validate tool permissions.

1

RAG pipeline basics

2

Embeddings and vector search

3

Short-term memory design

4

Long-term memory strategies

5

Context management methods

6

Memory poisoning risks

AI Integration

Analyze retrieval outputs with AI to improve grounding and context relevance
Support memory design by identifying persistence risks and context gaps

Activities/Case Study

Hands-on: configure memory flows.
Case study: long-task reliability.
Simulation: debug context drift.

1

LangChain and LangGraph overview

2

CrewAI workflow concepts

3

Google ADK foundations

4

Low-code agents with n8n

5

Code-first versus low-code

6

Framework selection criteria

AI Integration

Analyze framework options with AI to match delivery constraints and complexity
Support workflow assembly by generating starter logic for orchestration steps

Activities/Case Study

Hands-on: compare framework flows.
Case study: platform selection.
Simulation: build orchestration logic.

1

Roles and delegation models

2

Coordination and shared state

3

Failure handling strategies

4

MCP integration concepts

5

A2A communication patterns

6

AGENTS.md convention usage

AI Integration

Analyze agent responsibilities with AI to improve delegation and coordination logic
Support protocol planning by generating secure interaction flows for connected agents

Activities/Case Study

Hands-on: design agent roles.
Case study: protocol integration.
Simulation: coordinate agent handoffs.

1

Agent performance metrics

2

Test-suite design methods

3

LLM-as-judge evaluation

4

Long-horizon task testing

5

Debugging failed runs

6

Iterative improvement cycles

AI Integration

Analyze run traces with AI to detect failure patterns and weak steps
Support evaluation design by generating test cases and scoring rubrics

Activities/Case Study

Hands-on: create test suites.
Case study: failed agent runs.
Simulation: score task success.

1

Prompt injection defenses

2

Tool misuse controls

3

Context poisoning prevention

4

OWASP agentic risks overview

5

Bias, privacy, transparency

6

Human oversight checkpoints

AI Integration

Analyze threat scenarios with AI to identify likely attack paths and controls
Support governance planning by drafting oversight checkpoints and accountability notes

Activities/Case Study

Hands-on: assess security risks.
Case study: governance failures.
Role-play: approve risky actions.

1

Observability and logging design

2

Cost and latency control

3

Scaling and enterprise integration

4

Capstone build and test

5

Design note preparation

6

ROI business case presentation

AI Integration

Analyze operational logs with AI to surface cost, latency, and reliability issues
Support capstone delivery by generating presentation drafts and business case summaries

Activities/Case Study

Hands-on: finalize capstone agent.
Case study: production rollout.
Role-play: present deployment plan.

1

Participants act as an internal AI engineering team tasked with designing an agentic solution for a high-volume enterprise service workflow.

2

The scenario involves requests that require tool access, policy checks, knowledge retrieval, approvals, and status updates across connected systems.

3

Teams must decide whether a single-agent, workflow-based, or multi-agent design is most appropriate.

4

They also need to define memory boundaries, tool permissions, escalation points, and failure handling before implementation begins.

5

Working in online breakout teams, participants design the architecture, build a bounded prototype, and test it against realistic task scenarios and edge cases.

6

They evaluate task success, tool-call accuracy, and failure patterns while applying security and governance controls.

7

Each team then presents its deployment approach, observability plan, and business value case.

8

The outcome is a practical view of how agentic AI can automate complex workflows without losing control, accountability, or operational clarity.

1

Participants work in a simulated online environment that mirrors common enterprise integration conditions for agentic AI delivery.

2

They design automated workflows using relevant tools from the course, including agent frameworks, APIs, retrieval components, and orchestration logic.

3

The sandbox shows where delays, context loss, weak permissions, and poor handoffs can disrupt outcomes.

4

This gives participants a safe setting to streamline task execution, improve reliability, and test production-minded design choices before wider deployment.

5

Using generative AI and automation methods, participants create prompts, tool schemas, memory flows, evaluation checks, and workflow logic for a bounded business task.

6

They produce a working agent prototype, test scenarios, design notes, and a concise business case.

7

Governance remains visible throughout through approval points, logging expectations, and least-privilege controls.

8

The result is a hands-on build experience that balances speed, quality, security, and enterprise readiness.

Meet your Trainer

Our Trainers

Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.

We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.

Our Trainers

Learning Outcomes

After finishing the course, you will be able to:

1

Distinguish agentic AI from chatbots, RPA, and standard LLM applications

2

Choose appropriate architectures for dependable enterprise automation needs

3

Generate structured, grounded outputs for complex multi-step agent workflows

4

Connect secure enterprise tools through least-privilege access controls

5

Analyse agent failures with traces, metrics, tests, and iterative refinements

6

Deliver production-ready agent solutions with governance and ROI justification

objective-image

Ready to get started?

KHDA Certificate

Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.

Certifcate-Image0

Learners Point Certificate

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

Certifcate-Image1

Overall ratings by our students

Related courses

Learn now, pay later

Dive into your course now and pay in installments

Tamara

ADCB

Frequently asked questions

The Certification in Agentic AI Design and Development in Dubai is a 32-hour practical program that teaches participants how to design, build, and govern reliable AI agents for complex enterprise tasks.

It focuses on turning agent concepts into controlled solutions through architecture design, tool integration, memory management, multi-agent coordination, testing methods, and security practices. Participants work with current frameworks and interoperability protocols, then apply these skills in a structured capstone that reflects real production conditions across the UAE.

This Agentic AI training in Dubai is designed for software engineers, AI practitioners, architects, and automation developers who already work with Python, APIs, and basic LLM applications.

It is suited to professionals who want to move beyond chatbot prototypes and develop bounded agents for enterprise workflows. The program is particularly relevant to technical professionals responsible for agent architecture, automation, tool integration, testing, or governance across business environments in the UAE.

Participants gain hands-on experience that mirrors real enterprise conditions through structured simulations, sandbox builds, and a full capstone project.

The practical components include:

Industry simulation for designing an enterprise service request agent
Automation sandbox for building and testing agent workflows
Guided labs covering tool integration, memory, and evaluation
Framework comparison exercises using code-first and low-code platforms
Capstone project with prototype testing and design documentation
Business case presentation focused on deployment and operational value

Yes, the Agentic AI Design course in Dubai is available online and can be accessed by participants across the UAE and international locations.

The training is delivered fully online over 32 hours, allowing technical professionals to complete guided labs, framework exercises, and the capstone project without attending a physical classroom. This format supports flexible participation while maintaining the practical focus required for designing and governing production-ready AI agents.

The course introduces participants to agent frameworks, development approaches, and interoperability protocols used for designing and coordinating AI agent systems.

The training covers the following:

LangChain and LangGraph for agent workflow design
CrewAI for multi-agent role and coordination patterns
Google ADK foundations for structured agent development
Low-code agents using n8n
MCP for tool and system integration
A2A communication patterns and AGENTS.md conventions

Yes, multi-agent systems form a core part of this training program. Participants learn how to design roles, manage delegation, coordinate shared state, and handle failures across multiple agents.

The module also covers practical interoperability through MCP integration, A2A communication patterns, and the AGENTS.md convention, enabling participants to build coordinated AI agent workflows suited to complex enterprise tasks.

Learners Point delivers this program with a practical focus on agent engineering, supported by structured labs, simulations, and a capstone project. The training also covers architecture, tool integration, multi-agent systems, testing, security, and governance.

Key reasons Learners Point is chosen for this training:

Over two decades of professional development experience in the region
Strong emphasis on practical labs, simulations, and a full capstone project
Clear coverage of architecture, tools, multi-agent systems, testing, and governance
Online delivery suited to working professionals across the UAE
Focus on controlled and observable agent deployment
Certification awarded by Learners Point upon successful completion

Do you want to learn more about Learners Point Academy?

Learn more about courses
Understand about our methodology
Let’s talk about Corporate trainings
Anything else that you want to know, we are here for you!

Let's chat!

Name

Phone

Email

I would like to discuss