Agentic AI Certification Program

Learn prompt systems, Python AI APIs, RAG, vector search, function calling, tool-based agents, and business automation workflows through portfolio-ready builds.

Cohort: Starting Soon
Duration: 4 months
Emma ThompsonJames WalkerMeera Iyer
10,000+ Trained

Learner Reviews

4.9

Based on 27 first-party learner reviews

These ratings come from learner feedback published on this page and are mirrored in the course structured data for search engines.

Career Outcomes

Target Roles
  • AI Specialist
  • Automation Consultant
  • AI Operations Executive
  • AI Workflow Builder
Average Salary (UK)
£35,000 – £75,000+

Post-completion

Industry Demand

Growing demand across startups, agencies, enterprises

Is This Right Fit For You?

Professionals exploring AI specialist and automation careers
Marketers, operators, analysts, and founders wanting practical AI workflows
Beginners who want to build assistants, agents, and automations step by step
Anyone searching for an Agentic AI course UK with portfolio-ready projects

Curriculum

The full 16-week Agentic AI curriculum is expanded below, including phase-by-phase topics, weekly labs, agent workflow design, and portfolio outcomes.

View detailed curriculum

Overview

Duration4 months
FormatPractical + tool-based
LevelBeginner-friendly

Course Investment

£1,999
EMI Available

Tools Covered

CHChatGPT
OpenAI API
Python
JSJSON
LALangChain concepts
CHChromaDB / FAISS
ZAZapier
MAMake
GitHub
STStreamlit / Flask

Applied agentic AI certification plan

Agentic AI Automation Certification

A four-month practical programme for building AI assistants, RAG systems, tool-calling agents, and business automation workflows. Learners finish with deployable demos, GitHub evidence, and responsible AI documentation.

16 weeks64 guided hours6 portfolio projectsAgents, RAG, APIs, automation

Role readiness

AI specialist, automation consultant, AI operations, and workflow automation roles

Real AI systems

Learners build assistants that retrieve, call tools, validate outputs, and hand off safely

Governed automation

Privacy, redaction, permissions, logging, evaluation, and human approval are built into the workflow

Recommended curriculum balance

Start with judgement, then build agentic systems step by step.

The programme keeps AI foundations and prompting practical, then spends most of the time on Python, APIs, RAG, function calling, orchestration, deployment, and portfolio proof.

AI and LLM Foundations

2 weeks

8 live hours

Core concepts, model behavior, limitations, and safe AI use

Prompt Engineering

2 weeks

8 live hours

Repeatable prompt systems for business, research, and operations

Python and APIs

3 weeks

12 live hours

The technical base for building AI-powered tools and automations

RAG and Vector Search

3 weeks

12 live hours

Document search, knowledge assistants, embeddings, and evaluation

Agentic Workflows

4 weeks

16 live hours

Tool use, planning, memory, orchestration, testing, and deployment

Portfolio and Career

2 weeks

8 live hours

GitHub proof, case-study storytelling, interviews, and responsible AI documentation

Detailed syllabus

Five phases from AI foundations to deployable agent workflows.

Learners move from prompt systems to Python automation, RAG, tool-calling agents, orchestration, deployment, and interview-ready project storytelling.

Phase 1

AI Foundations and Prompt Systems

Build a clear understanding of how generative AI works, where it fails, and how to design prompts that produce reliable business outputs.

AI and LLM Foundations

Weeks 1-2

Project: AI Use-Case Audit
AI, machine learning, generative AI, LLMs, tokens, context windows, latency, cost, and model trade-offs
Prompt structure, role prompting, task framing, examples, constraints, output formats, and evaluation criteria
Hallucination, bias, privacy, data sensitivity, human review, and responsible use in UK business settings
Business use-case discovery across operations, sales, support, HR, marketing, research, and analytics
Applied AI workflow

Learners use AI to map real workflows, identify automation candidates, compare risk levels, and produce a practical AI adoption memo.

Prompt Engineering for Workflows

Weeks 3-4

Project: Business Prompt Library
Prompt patterns for summarisation, extraction, rewriting, classification, reasoning support, and decision prep
Reusable prompt templates, prompt variables, rubric-based quality checks, and prompt versioning
Structured outputs, JSON schemas, tables, checklists, email drafts, research briefs, and meeting summaries
Evaluation loops, adversarial testing, edge cases, and when a prompt should become an automated workflow
Applied AI workflow

Learners build a tested prompt library with input examples, expected outputs, quality rubrics, and human verification notes.

Phase 2

Python, APIs and AI Automation Basics

Move from using AI tools manually to building controlled workflows with Python, APIs, JSON, environment variables, and reusable scripts.

Python for AI Builders

Weeks 5-6

Project: AI Utility Notebook
Python setup, notebooks, variables, control flow, functions, files, errors, packages, and virtual environments
Working with CSV, Excel, PDFs, text files, JSON, web data, and lightweight data cleaning
API requests, response handling, environment variables, rate limits, logging, and simple automation scripts
Building reusable helper functions for research, summarisation, extraction, classification, and reporting
Applied AI workflow

Learners use AI as a coding assistant while still reading, testing, and explaining every generated script before using it.

AI APIs and Structured Outputs

Week 7

Project: Structured Extraction Workflow
OpenAI API basics, message design, model selection, parameters, cost awareness, and response validation
Structured outputs for reliable JSON, schemas, validation checks, fallback handling, and retry logic
Batch processing, document extraction, classification pipelines, and simple business rule checks
Writing clear system instructions, separating data from instructions, and reducing prompt injection risk
Applied AI workflow

Learners build a workflow that converts messy text or documents into reviewed structured JSON for a business process.

Phase 3

RAG, Vector Databases and Knowledge Assistants

Create assistants that search trusted documents before answering, with a strong focus on retrieval quality, citation discipline, and evaluation.

Embeddings and Semantic Search

Weeks 8-9

Project: Document Search Prototype
Embeddings, chunks, metadata, similarity search, ranking, retrieval precision, and recall
Vector database concepts using tools such as ChromaDB, FAISS, or managed vector storage
Document preparation, chunking strategy, metadata design, access control, and update workflows
Search evaluation, answer grounding, citation checks, and common retrieval failure modes
Applied AI workflow

Learners build semantic search over a controlled document set and test whether answers are grounded in retrieved evidence.

Retrieval Augmented Generation

Week 10

Project: Policy or Knowledge Q&A Assistant
RAG architecture, retriever plus generator flow, context assembly, citations, and answer constraints
Prompt injection risks, conflicting documents, outdated documents, refusal behavior, and escalation rules
Evaluation sets, golden answers, trace review, relevance scoring, and user feedback loops
Designing knowledge assistants for HR policies, training content, customer support, operations, or sales enablement
Applied AI workflow

Learners turn document search into a question-answering assistant that cites sources and flags low-confidence responses.

Phase 4

Agentic Workflows and Tool Use

Design systems that can choose tools, follow steps, call functions, check intermediate results, and complete business tasks under guardrails.

Function Calling and Tool-Based Agents

Weeks 11-12

Project: Tool-Calling Agent
Function calling, tool schemas, inputs and outputs, validation, deterministic business rules, and guardrails
Connecting AI to calculators, files, spreadsheets, databases, CRMs, ticketing tools, and internal APIs
Planning loops, step-by-step execution, intermediate checks, error recovery, and human approval points
Testing tool outputs, logging traces, debugging agent decisions, and preventing runaway automation
Applied AI workflow

Learners build an assistant that chooses from defined tools and completes a bounded task with visible reasoning checks and logs.

Workflow Automation and Orchestration

Weeks 13-14

Project: Business Automation System
Workflow mapping, triggers, actions, routing rules, approvals, notifications, and exception handling
Automation with no-code tools such as Zapier or Make alongside Python scripts and API workflows
LangChain-style orchestration concepts, chains, agents, retrievers, tools, memory, and observability
Security, secrets, permissions, audit trails, monitoring, handoff design, and production-readiness checks
Applied AI workflow

Learners automate a real process such as lead triage, support routing, research reporting, invoice review, or onboarding assistance.

Phase 5

Deployment, Portfolio and Career Acceleration

Package AI systems into credible portfolio evidence with GitHub documentation, demos, governance notes, and interview-ready explanations.

Deployment and Product Thinking

Week 15

Project: AI Workflow Demo App
Building lightweight demos with Streamlit, Flask, or a simple web interface
User flows, input validation, loading states, failure states, cost limits, monitoring, and feedback capture
Deployment options, environment variables, secrets, README files, demo scripts, and basic maintainability
Product thinking for AI: user value, risks, controls, success metrics, and when not to automate
Applied AI workflow

Learners package one workflow into a small demo that a stakeholder can test without reading the code first.

Career Services and Interview Prep

Week 16

Project: Agentic AI Portfolio Pack
Git and GitHub workflow, project READMEs, architecture diagrams, demo videos, and case-study writing
CV, cover letter, LinkedIn, and GitHub optimization for AI specialist and automation roles
Communication skills, project walkthroughs, AI ethics explanation, and mock technical interviews
Freelance positioning, consulting use cases, stakeholder discovery questions, and proposal structure
Applied AI workflow

Learners use AI to refine portfolio narratives and interview answers while keeping claims accurate, specific, and evidence-backed.

Week-by-week teaching plan

Saturday concepts, Sunday build labs, portfolio evidence every week.

The weekend rhythm keeps the programme practical: Saturday introduces architecture and concepts; Sunday is for guided builds, debugging, evaluation, and documentation.

Week
Saturday class
Sunday class
Project milestone
Week 1
AI landscape, LLM concepts, model strengths and limits
Use-case discovery lab and safe AI handling
Start AI Use-Case Audit
Week 2
Responsible AI, privacy, bias, hallucinations, human review
Workflow mapping and automation opportunity scoring
Submit AI Use-Case Audit
Week 3
Prompt patterns for summary, extraction, classification
Prompt templates, variables, examples, and constraints
Start Business Prompt Library
Week 4
Structured prompting, output formats, rubrics
Evaluation loops, edge cases, prompt versioning
Submit Business Prompt Library
Week 5
Python setup, notebooks, variables, control flow
Functions, files, errors, packages, and environments
Start AI Utility Notebook
Week 6
Working with text, CSV, Excel, JSON, and PDFs
API requests, logging, reusable helper functions
Build automation helper scripts
Week 7
AI APIs, model parameters, cost awareness
Structured outputs, validation, retries, batch workflows
Submit Structured Extraction Workflow
Week 8
Embeddings, chunking, metadata, similarity search
Vector database setup and document preparation
Start Document Search Prototype
Week 9
Search evaluation, ranking, retrieval failure modes
Citation checks and answer grounding
Submit Document Search Prototype
Week 10
RAG architecture and context assembly
Knowledge Q&A assistant build lab and evaluation
Submit Policy or Knowledge Q&A Assistant
Week 11
Function calling, tool schemas, validation
Connect AI to files, calculators, APIs, and business rules
Start Tool-Calling Agent
Week 12
Planning loops, error recovery, human approval
Agent debugging, logs, traces, and guardrail tests
Submit Tool-Calling Agent
Week 13
Workflow mapping, triggers, actions, approvals
Zapier/Make plus Python automation patterns
Start Business Automation System
Week 14
Orchestration, memory, monitoring, audit trails
Production-readiness checklist and stakeholder demo
Submit Business Automation System
Week 15
Demo app structure, user flow, failure states
Deploy or package AI Workflow Demo App
Submit AI Workflow Demo App
Week 16
GitHub portfolio, README, architecture notes
Mock interviews, CV, LinkedIn, and project defense
Submit Agentic AI Portfolio Pack

Portfolio outcomes

Six projects that prove practical agentic AI ability.

Each submission shows a business problem, workflow design, technical implementation, evaluation evidence, and responsible AI controls.

AI Use-Case Audit

Identify where AI should and should not be used in a business process

Workflow map, use-case shortlist, value-risk matrix, and responsible AI notes

Business Prompt Library

Create repeatable AI workflows instead of one-off prompts

Prompt templates, sample inputs, expected outputs, quality rubric, and version notes

Structured Extraction Workflow

Turn messy information into reliable structured data for operations

Python notebook or script, schema, JSON output examples, and validation checks

Policy or Knowledge Q&A Assistant

Build a grounded assistant that answers from trusted documents

RAG prototype, document set, retrieval tests, citations, and failure-mode notes

Business Automation System

Design agentic workflows that connect AI to tools under guardrails

Automation map, tool-calling flow, approval points, logs, and stakeholder demo

Agentic AI Portfolio Pack

Present AI automation work credibly in interviews and client conversations

GitHub repo, README, architecture diagram, demo video, governance note, and CV bullets

Required submission pattern

Business problem
Workflow map
Tools and data sources
Prompt or agent design
Testing evidence
Demo output
Risk controls
Human review plan

Build before abstract theory

Learners create working prompts, scripts, RAG prototypes, agents, and automations early so concepts stay tied to visible outputs.

Document like a professional

Every project includes README notes, architecture choices, testing evidence, failure modes, and clear human review checkpoints.

Prepare for stakeholders

The programme trains learners to explain value, risk, cost, limits, and governance to managers, clients, and interview panels.

Simple course promise

Teach AI judgement first, then build practical assistants, RAG systems, tool-calling agents, and automations that are documented, tested, and safe enough to discuss with real stakeholders.

Career guides

Read before choosing this programme

These Brit Institute guides explain the UK roles, salaries, tools, and learning path connected to this course.

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Hands-On Projects

  • AI Use-Case Audit
  • Business Prompt Library
  • Structured Extraction Workflow
  • Policy or Knowledge Q&A Assistant
  • Business Automation System
  • Agentic AI Portfolio Pack

Career Support

  • AI portfolio and GitHub project review
  • Use-case based interviews and project walkthroughs
  • Freelance, consulting, and job guidance

Learner Reviews

4.9 / 5 average from 27 published learner reviews

Kiran Patel

Published 22 Feb 2026

5.0 / 5

This course made automation feel approachable. I built useful workflows quickly and started spotting repetitive tasks at work that I could actually improve.

Noah Mensah

Published 11 Jan 2026

4.9 / 5

I liked how practical the sessions were. We were not just watching demos; we were designing flows, testing tools, and understanding where automation can fail.

Fatima Rahman

Published 15 Dec 2025

4.8 / 5

The course helped me connect AI tools with real business operations. I now feel much more prepared to discuss automation ideas with my team.

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