Certification in Agentic AI Development
40-hour training covering complete agentic AI lifecycle
Secure AI agents for multi-step business workflows
Agent architecture, ReAct, tools, RAG, memory & API integration
LangChain, LangGraph, CrewAI, Google ADK, n8n, MCP & A2A
Multi-agent workflows, evaluation test suites & debugging skills
Enterprise agent simulation, automation sandbox & capstone project
Flexible classroom & live online learning options
4.9/55382 EnrolledEnrolled Learners
4.9/55382 EnrolledEnrolled Learners
Overview
What you will learn:
- Identify suitable agentic AI use cases and define appropriate levels of autonomy
- Design bounded agent architectures, tools, states, and controls
- Develop reliable prompts, structured outputs, and model-selection strategies
- Build custom tools, function-calling workflows, and secure API integrations
- Implement RAG pipelines using embeddings, vector search, and memory flows
- Build and orchestrate multi-agent workflows using LangChain, LangGraph, CrewAI, MCP, and A2A
- Evaluate AI agents using test suites, debugging techniques, security controls, and governance practices
- Plan deployment with observability, cost control, and the capstone project
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.
Learning Outcomes
After the training is completed, you will be able to:
1
Differentiate between agentic AI, chatbots, RPA, and conventional LLM applications
2
Select suitable architectures for reliable enterprise automation requirements
3
Produce structured, grounded outputs across complex multi-step agent tasks
4
Integrate secure enterprise tools using least-privilege access controls
5
Diagnose agent failures using traces, metrics, tests, and iterative improvements
6
Present production-ready agent solutions with governance and ROI justification
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.
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
Overall ratings by our students
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Frequently asked questions
The Certification in Agentic AI Development is a 40-hour practical program for creating AI agents that can plan, use tools, retrieve knowledge, and complete controlled multi-step business tasks. Participants gain a grounded understanding of when an agentic approach is appropriate and how to set meaningful limits.
Rather than focusing only on theory, participants practise making sound engineering choices around autonomy, workflow design, failure recovery, and accountability. The course builds the judgement needed to translate real operational requirements into agent solutions that remain transparent and aligned with enterprise policies.
Software engineers, AI practitioners, solution architects, and automation developers should enrol in an Agentic AI Design and Development course. The course helps professionals create AI systems that can reason, use tools, manage context, and reliably complete multi-step tasks. It is particularly useful for professionals working with LLM applications, enterprise APIs, workflow automation, or connected business systems.
This program also suits technical specialists who must balance innovation with control, including those responsible for agent testing, security, governance, observability, and responsible deployment. Participants should be familiar with Python, APIs, and basic LLM concepts to gain the greatest value from the hands-on curriculum.
This Agentic AI Design and Development training is not designed for complete beginners with no technical foundation. It is best suited to participants who already understand Python, APIs, and basic LLM applications and want to move from simple AI prototypes to structured agent engineering.
The course introduces core agent concepts clearly, but quickly moves into practical work involving function calling, RAG, memory, frameworks, testing, security, and deployment. Participants new to AI should first build foundational confidence in programming and LLM application development.
Agentic AI is designed to pursue defined goals through planning, tool use, memory, and controlled multi-step actions. In contrast, chatbots mainly conduct conversations, while conventional LLM applications typically generate or analyse content from a prompt without independently coordinating an extended workflow.
RPA automates predictable, rule-based processes using fixed instructions and interfaces. Agentic AI is better suited to tasks that require contextual reasoning, information retrieval, adaptive decisions, and escalation when needed. Participants learn to select the right approach based on task complexity and oversight needs.
The course covers LangChain, LangGraph, CrewAI, Google ADK, and n8n for agentic AI development and workflow orchestration. Participants explore how each option supports different build approaches, from code-first agent engineering to low-code automation for connected enterprise processes.
Rather than treating frameworks as interchangeable, the training helps participants assess delivery complexity, orchestration requirements, integration needs, and operational constraints. They compare framework flows and apply selection criteria to choose platforms that support maintainable AI agent workflows.
The Agent Workflow Automation Build Lab is the course’s practical sandbox for turning agentic AI concepts into tested workflow designs. It gives participants a realistic environment to assemble prompts, tool schemas, retrieval components, memory flows, and orchestration logic for a defined business task.
Participants examine how context loss, weak permissions, delayed actions, and poor handoffs affect outcomes. They refine workflows using evaluation checks, approval points, and logging expectations, gaining disciplined experience in balancing automation speed, security, reliability, and enterprise readiness before wider deployment.
Yes. The course includes an Agentic AI capstone project in which participants finalise and test a working agentic solution for a defined business task. The capstone brings together the program’s design, tooling, memory, evaluation, security, and operational planning concepts.
Participants also prepare a design note, deployment approach, and concise ROI business case. This final exercise builds confidence in explaining technical choices, expected value, and readiness considerations to both technical and business stakeholders.
This Agentic AI training supports career growth in enterprise AI development, workflow automation, solution design, and AI governance. Participants can pursue opportunities that match their prior technical experience, portfolio quality, and employer requirements.
Career opportunities after completing this training:
- Agentic AI Engineer: Designs, builds, tests, and improves goal-driven AI agents for enterprise workflows
- AI Agent Developer: Develops tool-enabled agents using APIs, memory, retrieval, and orchestration frameworks
- Generative AI Engineer: Builds dependable LLM applications, structured outputs, and retrieval-augmented AI solutions
- AI Solutions Architect: Defines scalable agent architectures, integration patterns, controls, and deployment approaches
- AI Automation Developer: Automates connected business processes through agent workflows, tools, and low-code orchestration
- AI Governance and Security Specialist: Strengthens AI-agent oversight, risk controls, prompt-injection defence, and responsible deployment
Learners Point’s Agentic AI Design and Development training connects technical capability with enterprise delivery discipline. Participants gain hands-on experience in building reliable AI-agent solutions for real workflow challenges.
Following are the reasons to choose Learners Point for this training:
- Enterprise-aligned curriculum spanning design, testing, governance, and deployment
- Hands-on learning through labs, simulations, framework comparisons, and practical exercises
- Realistic build experience through the automation sandbox and enterprise service simulation
- Current agentic AI coverage including MCP, A2A, multi-agent workflows, and observability
- Production-minded capability across security, human oversight, failure controls, and cost management
- Live online international delivery for participants across UAE, Saudi Arabia, Africa, MENA, and Europe
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