Agentic AI Engineering Bootcamp
capability building,
designed for your organisation.
A custom-built corporate programme for AI engineers, ML engineers, senior software engineers, backend developers, and solution architects with 3+ years of experience who will design, build, and ship production-grade agentic systems. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- Agent = LLM + tools + loop. The minimal definition and its implications
- Agents vs. chains vs. pipelines vs. workflows — when each is the right abstraction
- Autonomy levels: scripted, supervised, semi-autonomous, autonomous — and the operational implications of each
- Anti-pattern: when adding agency makes the system worse, not better
Want the full module-by-module syllabus, sample assignments, and pricing?
One PDF — sent to your inbox in under a minute.
Demonstrable skills your team will apply on live projects.
Design agent architectures that fit the use case
Single-agent, supervisor-worker, planner-executor, critic-loop, hierarchical — applied based on task structure, not framework default.
Ship production-grade agents on OpenAI Agents SDK or LangGraph
Capstone-grade agentic systems with full observability, evaluation, guardrails, cost controls, and graceful degradation.
Operate multi-framework fluency
OpenAI Agents SDK, CrewAI, LangGraph, AutoGen — selected with judgement, not by default.
Pass GSDC Agentic AI Engineer Certification
Two attempts; cohort first-attempt pass rate 88%.
Lead an agentic AI initiative end-to-end
Equipped to architect, build, and own production agentic systems for the organisation.
Cut prototype-to-production time by 60%+
The discipline gap between 'works in notebook' and 'survives in production' — closed during the bootcamp.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Has built ChatGPT-style chat applications but never shipped a multi-step or multi-agent system
- ·Familiar with LangChain at a chain level but not with agent orchestration patterns
- ·No working knowledge of OpenAI Agents SDK, CrewAI, LangGraph, or AutoGen
- ·Cannot reliably implement tool use, function calling, structured output across agent flows
- ·No production discipline — agents demoed in notebooks but not deployed, monitored, evaluated
- ·Limited mental model of when an agent is the right pattern vs. when a pipeline is
Where they'll arrive
- ✓Architectural fluency — can design and defend single-agent, supervisor-worker, planner-executor, and critic-loop patterns based on use case
- ✓Framework portability — fluent in OpenAI Agents SDK, LangGraph, CrewAI, AutoGen with informed views on framework selection
- ✓Tool-use mastery — implements robust function-calling, MCP-server integration, and external API orchestration with retry, fallback, and validation
- ✓Production discipline — agents shipped with traces, evals, guardrails, cost dashboards, and rollback paths
- ✓Multi-agent orchestration — has built and demoed working multi-agent systems with appropriate role specialisation and communication patterns
- ✓MCP-native development — fluent in the Model Context Protocol for portable tool/resource integration
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
Generative AI creates content such as text, images, code, reports, emails, summaries, and ideas based on user instructions. Agentic AI goes one step further: it can plan tasks, use tools, call APIs, retrieve data, make decisions within defined boundaries, and complete multi-step workflows with less human supervision. In simple terms, Generative AI helps you create, while Agentic AI helps you execute. McKinsey describes agentic AI as autonomous systems designed to make decisions, plan, and take action toward predefined goals.