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Certified Agentic AI & Automation Professional
Design, build, and deploy autonomous AI agents that plan, reason, and act
A structured, hands-on online program for technology professionals, developers, and data practitioners who want to move beyond chatbots — and build AI systems that autonomously pursue goals, use tools, call APIs, and coordinate with other agents to complete complex, multi-step tasks.
Industry-aligned curriculum · Structured assessments · Career support included
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Why Agentic AI is the defining skill of the next decade
AI is no longer a research topic. Autonomous agents are already reshaping software engineering, data analysis, customer operations, and enterprise automation — and the organisations building with them are pulling ahead.
Global AI market size
Revenue in USD billion (2022–2030E, illustrative)AI Engineer job growth
Year-on-year growth in AI/ML engineering roles (%)Where AI agents are deployed
Enterprise use-case breakdown (illustrative)Global AI market by 2030 (PwC estimate)
plan to deploy AI agents within 2 years (Gartner)
AI-assisted software teams vs traditional (GitHub)
AI Engineer — LinkedIn Emerging Jobs Report
Figures are industry-cited estimates and research references for educational context. They are not financial projections or guarantees of any outcome.
Agentic AI skill demand vs supply
Talent gap index by skill area (illustrative, higher = greater shortage)The talent gap is real — and widening
Organisations are racing to deploy autonomous AI systems, but the pool of practitioners who can architect, build, and safely deploy them remains thin. This is the opportunity for professionals who act now.
- LangChain, LangGraph, and agent skills are among the fastest-rising keywords in AI job postings
- FAANG, consulting, and enterprise firms are actively creating "AI Engineer" and "Agent Engineer" roles
- Agentic workflows are replacing bespoke automation in data pipelines, customer support, and dev tooling
- Responsible deployment skills — safety, evaluation, guardrails — are the hardest to find
The shift from AI assistant to AI agent
The next generation of AI systems don't just respond — they act. This program gives you the technical depth to design and deploy them.
Built around agent architecture
From perception and reasoning to memory and tool use — you learn the components that make AI systems autonomous, then integrate them into production-grade workflows and multi-agent pipelines.
For developers and practitioners
Whether you work in software engineering, data science, ML, product, or automation — the program meets you with practical, code-first modules and real project deliverables.
LLM Reasoning
Prompt engineering, chain-of-thought, ReAct, tree-of-thought, and structured output.
Tool Use & APIs
Equip agents with code execution, web search, databases, and third-party APIs.
Multi-Agent Systems
Orchestrate LangGraph, CrewAI, and AutoGen pipelines for collaborative agent workflows.
AI is moving from assistant to autonomous actor
Organisations are deploying AI agents that browse the web, write and run code, manage files, call APIs, and hand off tasks to other agents — with minimal human intervention. The professionals who understand how to architect these systems will define where enterprise AI goes next.
- Agents that plan and execute multi-step goals autonomously
- Systems with persistent memory and dynamic tool access
- Multi-agent pipelines that collaborate, delegate, and self-correct
- Production-grade deployment with monitoring and guardrails
Built for practice, not passive watching
- Code-first, implementation-led curriculum
- Mentor-led live sessions with structured feedback
- Real-world projects on LangChain, LangGraph, CrewAI
- Safety, ethics, and responsible deployment embedded throughout
- Capstone autonomous agent for your professional portfolio
- Career support: resume, interviews, role mapping
Designed for practitioners ready to build agents
This is not an introductory AI survey. It is a structured technical program for professionals who want to design and deploy agentic systems.
Software Engineers
Extend your skills to agentic workflows, tool-using LLMs, and autonomous pipelines.
Data Scientists & ML Engineers
Move from model training to deploying AI systems that act on predictions.
AI / ML Practitioners
Deepen your LLM knowledge into agent architectures, RAG, and memory systems.
Automation & DevOps Engineers
Build AI-powered automation that orchestrates tools, APIs, and workflows.
Product Managers in AI Teams
Understand what agents can and cannot do — and how to scope agentic products credibly.
Career Switchers into AI Engineering
Structured path from foundational Python and LLMs to production-grade agent development.
Built differently — because the field demands it
Most AI courses stop at prompting or fine-tuning. This program goes further — into the architecture, frameworks, and production practices that define real agentic AI work.
Code-first from day one
Every concept is implemented, not just explained. You write agents, build pipelines, and deploy systems — not watch slides about them.
12 implemented projectsThe full agent stack
LLM reasoning → tool use → memory → multi-agent orchestration → safety → deployment. A curriculum designed end-to-end, not topic by topic.
End-to-end progressionFramework-native training
You learn LangChain, LangGraph, CrewAI, and AutoGen as they are actually used in production — with real codebase patterns, not toy examples.
Industry frameworksSafety is not an afterthought
A dedicated module on evaluation, guardrails, prompt injection defences, and responsible deployment. The most overlooked skill in agentic AI.
Production-grade safetyDeployment and observability
You don't just build agents — you containerise, serve, monitor, and cost-manage them. LangSmith, FastAPI, Docker, and cloud endpoints included.
Production deploymentA capstone worth showing
The final project is a real multi-agent system deployed to a cloud endpoint with documentation, an architecture review, and a mentor-supervised demo.
Portfolio-ready capstone12 modules — from LLM fundamentals to multi-agent deployment
A progression designed for working professionals: build knowledge and code in every session.
Foundations of Large Language Models
Transformers, tokenization, prompting, and the LLM API landscape. Understand what LLMs do well — and where agents are required.
Introduction to AI Agents
Agent architecture: perception, reasoning, action, and memory. ReAct, chain-of-thought, and tree-of-thought reasoning patterns.
Tool Use & Function Calling
Equipping agents with tools: web search, code execution, database lookup, and third-party APIs. OpenAI function calling and tool schemas.
Memory Systems for Agents
Short-term, long-term, semantic, and episodic memory. Vector stores (FAISS, Chroma, Pinecone), retrieval-augmented generation (RAG), and memory management strategies.
LangChain for Agent Development
LangChain chains, LCEL, agents, and runnables. Integrating LLMs, tools, and memory into coherent agent applications.
LangGraph — Stateful Agent Workflows
Graph-based agent orchestration with state management, cycles, and branching. Building complex, stateful reasoning pipelines.
Multi-Agent Systems: CrewAI & AutoGen
Designing and orchestrating crews of specialised agents. Role assignment, delegation, inter-agent communication, and task decomposition.
Planning & Task Decomposition
Goal decomposition, sub-task planning, self-critique, and reflection loops. Evaluating plan quality and recovering from failure.
Code Generation & Execution Agents
Agents that write, execute, and debug code. Sandboxed execution environments, interpreter agents, and data analysis workflows.
Evaluation, Safety & Guardrails
Evaluating agent reliability and output quality. Prompt injection defences, output filtering, constitutional AI principles, and responsible deployment practices.
Production Deployment
Containerising agents (Docker), serving via REST APIs (FastAPI), observability (LangSmith, Arize), cost management, and latency optimisation.
Capstone: Autonomous Agent Project
End-to-end design and deployment of a multi-tool, multi-agent system addressing a real business problem. Mentor-reviewed architecture review, demo, and documentation.
Your 6-month path to agentic AI
Structured milestones keep you progressing from foundations to a fully deployed autonomous agent.
Foundations Phase
Study: LLM fundamentals, agent architecture, tool use, and memory systems.
Build: Prompt lab, ReAct agent, RAG pipeline
Frameworks Phase
Study: LangChain, LangGraph, and multi-agent orchestration with CrewAI and AutoGen.
Build: LangChain agent, LangGraph workflow, multi-agent crew
Advanced Agents Phase
Study: Planning, code execution, evaluation, safety, and guardrails.
Build: Task planner, code agent, safety audit
Deployment & Capstone Phase
Study: Production deployment and end-to-end capstone autonomous agent project.
Build: Cloud-deployed agent + capstone portfolio
The agent technology stack
Hands-on exposure to the frameworks, platforms, and infrastructure used in production agentic systems.
LangChain
Chains, runnables, and agent executors
LangGraph
Stateful graph-based agent orchestration
CrewAI
Multi-agent role-based collaboration
AutoGen
Conversational multi-agent framework
Python
Primary implementation language
FastAPI
Agent REST API serving
Docker
Agent containerisation & portability
LangSmith
Observability, tracing & evaluation
FAISS / Chroma
Vector store & retrieval
OpenAI APIs
GPT-4o & function calling
Hugging Face
Open-source LLMs & tools
Cloud Endpoints
GCP / AWS / Azure deployment
Build, not just learn
Every module ends with a working deliverable — a portfolio of autonomous AI systems that demonstrates applied capability.
ReAct Agent from Scratch
Implement a reasoning-action agent without frameworks.
Working agent codeRAG Research Pipeline
Multi-source document retrieval and Q&A agent.
RAG system + evaluationLangGraph Workflow Agent
Stateful multi-turn task pipeline with branching logic.
LangGraph implementationMulti-Agent Crew
CrewAI crew for autonomous research + drafting.
Deployed crew pipelineCode Execution Agent
Agent that writes, runs, and debugs Python code.
Sandboxed code agentProduction Deployment
FastAPI-served agent with Docker and LangSmith monitoring.
Cloud endpoint + dashboardAn end-to-end autonomous AI system
Design and deploy a multi-tool, multi-agent system addressing a real business problem of your choice. Includes an architecture design document, full implementation, deployment to a cloud endpoint, mentor-reviewed demo, and professional README — a complete portfolio artefact for technical roles.
- Architecture design document
- Full implementation (LangChain / LangGraph / CrewAI)
- Cloud deployment with monitoring
- Live or recorded mentor-reviewed demo
- Professional project README
Reserve your place in the next cohort
Live, mentor-led batches with limited seats. Pick a start date that works for your schedule.
Earn a credential that reflects applied capability
On successful completion you will receive the Certificate of Completion — Certified Agentic AI & Automation Professional, awarded by Learning Saint.
Requirements to qualify
- Complete all 12 mandatory modules
- Submit all practical project assignments
- Meet minimum assessment thresholds
- Complete and present the capstone autonomous agent project
- Participate in scheduled mentor-led sessions
This certificate recognises structured training in agentic AI and automation for professional development. It is not a professional licence, regulatory credential, or employment guarantee.
Awarded by Learning Saint
Certificate of Completion
Certified Agentic AI & Automation Professional
- 6-month structured program
- Capstone-validated capability
- Portfolio-ready agent project
From program to placement — a structured path
We don't just teach the skills — we prepare you to interview for, land, and excel in the roles that need them.
AI Role Readiness
- Resume crafted for AI Engineer / LLM roles
- LinkedIn profile optimised for recruiter search
- GitHub portfolio review & README guidance
- Personal brand positioning for AI careers
Technical Interview Preparation
- LLM & agent architecture question drills
- Live mock interviews with mentor feedback
- System design for agentic AI scenarios
- Python & ML coding interview practice
Portfolio & Capstone Review
- Capstone project mentor review & polish
- Architecture doc & README for recruiters
- Presentation coaching for technical demos
- GitHub project structure review
Job Search Strategy
- Target role mapping by background and goal
- Recruiter outreach and network strategy
- AI-specific job board & company targeting
- Offer evaluation and negotiation guidance
AI Engineer compensation bands
Illustrative salary ranges by experience level (USD, global reference)Where AI engineers work
Sector distribution of open AI engineering roles (illustrative)Why this program — not a generic AI course
| Feature | Generic AI/ML Course | LLM Survey Videos | Certified Agentic AI & Automation Pro |
|---|---|---|---|
| Agentic architecture focus | Rarely | Rarely | Core curriculum |
| LangGraph / CrewAI / AutoGen | Partial | Variable | Dedicated modules |
| Memory, RAG & tool use | Limited | Limited | Implemented in projects |
| Production deployment | Rarely | No | Module 11 + capstone |
| Safety & guardrails | Brief mention | No | Dedicated module |
| Mentor feedback | Limited | No | Structured |
| Career support | No | No | Included |
From learner to agent builder
The LangGraph and multi-agent modules are where it clicked. I went from understanding what agents do conceptually to actually building a stateful pipeline I could demo to stakeholders.
The safety and deployment modules are what set this apart. Most AI courses stop at "here's the demo." This one taught me how to take it to production with proper monitoring and guardrails.
As a product manager I needed to understand what agents genuinely can and cannot do. The curriculum gave me the technical depth to scope agentic features credibly and challenge unrealistic proposals.
Enroll in five steps
- Submit your inquiryComplete the form or request a callback.
- Talk to an advisorDiscuss fit, prerequisites, and schedule.
- Profile review & guidanceWe map the program to your background.
- Confirm & onboardEnrollment, payment, and LMS access.
- Begin Module 1Kick off Foundations of Large Language Models.
Eligibility: Programming experience in Python is required. Familiarity with basic ML concepts is strongly recommended. Prior LLM or AI experience is helpful but not mandatory — dedicated foundation modules support learners entering from software engineering or data backgrounds.
Program Details
Questions, answered
A 6-month, mentor-led online program covering LLM foundations, agent architecture, tool use, memory, LangChain, LangGraph, CrewAI, safety, and production deployment — culminating in a capstone autonomous AI agent project.
Programming experience in Python is required. Familiarity with basic ML or data science is recommended. Prior LLM experience is helpful but not mandatory — the early modules provide the necessary foundations.
The curriculum goes beyond prompting and fine-tuning to focus specifically on agent architecture — tool use, memory systems, multi-agent orchestration, stateful workflows, safety, and production deployment.
LangChain, LangGraph, CrewAI, AutoGen, FastAPI, Docker, LangSmith, FAISS, Chroma, and cloud deployment environments. All are covered with practical implementation exercises.
Yes. Module 12 is a full capstone: design and deploy a multi-tool, multi-agent system with architecture documentation, code, cloud deployment, and a mentor-reviewed demo.
Hybrid — live mentor-led sessions with recorded access, flexible assignment schedules, and structured weekly milestones across 6 months.
Yes. The Certificate of Completion — Certified Agentic AI & Automation Professional is awarded upon completing all modules, assignments, and the capstone.
Yes. The program is designed for working professionals: live sessions are scheduled with recordings available, and assignments follow weekly milestones.
Yes — Module 10 is dedicated to evaluation, safety, guardrails, prompt injection defences, and responsible AI deployment practices.
No. Learning Saint provides career support and placement assistance. Employment outcomes depend on individual performance, market conditions, and employer requirements.
Download the program brochure
Curriculum, tools, projects, certification requirements, and admissions — all in one PDF.
Download Brochure Talk to an AdvisorInside the brochure
- 6 Months · Live Online
- 12 Modules · 10+ Projects
- Capstone Autonomous Agent
- Certificate on Completion
- Career Support Details
Start building autonomous AI systems — structured, practical, production-ready
Enroll in the Certified Agentic AI & Automation Professional program at Learning Saint — mentor-led instruction, hands-on projects, capstone mentoring, and career support in a flexible online format.
Practical, industry-aligned training. No unrealistic job guarantees.
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