Choose the right OpenAI surface
Separate engineering enablement with Codex from product development with the API, then connect them where the workflow benefits.
OpenAI enterprise training
Build with OpenAI Operate with discipline
Hands-on training for teams using Codex to improve engineering work and the OpenAI API to build reliable AI products and agents.
The outcome
Capability across the whole OpenAI delivery path
Build around the Responses API
Design model interactions, tools, state and structured outputs as an application contract rather than a collection of prompts.
Orchestrate agents with control
Use tools, handoffs, guardrails and tracing with explicit autonomy, approval and failure boundaries.
Evaluate before production
Turn representative examples into repeatable evaluations for quality, safety, latency and cost decisions.
Two surfaces
Know what belongs in engineering work and what belongs in the product
Codex and the OpenAI API solve different parts of the delivery problem. The programme makes that boundary explicit.
O / 01
Codex
Build stronger engineering workflows
For development teams using Codex across the app, terminal, IDE, cloud and remote environments with repository context and review.
- Task framing, context, instructions, skills and plugins
- Local, worktree, multi-agent, cloud and remote workflows
- Hooks, automations, review, permissions and safe delegation
O / 02
OpenAI API
Build reliable AI products and agents
For application teams integrating current OpenAI models, tools and agent workflows into production systems.
- Responses API, structured outputs and state
- Agents SDK, tools, handoffs, guardrails and tracing
- Evals, data controls and production operations
Built for
The people building, enabling and operating AI systems
Developers
Use Codex deliberately and build API integrations that remain testable, reviewable and maintainable.
Architects and platform teams
Make sound decisions about models, state, tools, identity, data controls, observability and operational ownership.
Engineering leaders
Connect platform access and enablement to safe delivery practices, measurable quality and adoption outcomes.
Programme
From first model call to controlled production change
The final agenda is matched to your development environment, application architecture and enterprise controls.
OpenAI delivery landscape
Choose the right product surface and architecture for the team and task.
- Codex, ChatGPT and API use-case boundaries
- Current model families and model-selection criteria
- Prototype, product and engineering workflow decisions
Codex for engineering teams
Turn an individual coding assistant into a controlled repository practice.
- App, CLI, IDE, cloud and remote development workflows
- Instructions, skills, plugins, hooks, tools and context
- Worktrees, multi-agent delegation, automations, review and verification
Responses API foundations
Build a clear application contract around model input, output and state.
- Responses, conversation state and streaming
- Structured outputs and function tools
- File, web and connected tool patterns where relevant
Agents and orchestration
Coordinate tools and specialised responsibilities without hiding system behaviour.
- Agents SDK, tools, handoffs and state
- Guardrails, approvals and human intervention
- Tracing, failure handling and orchestration choices
Multimodal and realtime patterns
Use the right interaction mode when text alone is not the product experience.
- Image and document inputs
- Realtime voice and event-driven sessions
- Latency, interruption and user-experience trade-offs
Evals and enterprise operation
Create a repeatable path from representative examples to controlled production change.
- Datasets, graders and regression evaluations
- Quality, latency, cost and safety measurement
- Projects, access, retention and data-control decisions
Programme brief
Built around your delivery context
We scope the cohort, product surfaces, languages, repositories and data constraints before deciding how deep the programme should go.
- Duration
- 2–3 days
- Audience
- Developers, architects, platform teams and engineering leaders
- Delivery
- Virtual or on-site
- Focus
- Codex, OpenAI API and agent delivery
- Approach
- Hands-on exercises, evaluation and production decisions
Practice-informed
Taught as an engineering system, not a prompt collection
Fedorai founder Arafat Tehsin is an applied AI leader, solution architect, Microsoft MVP in AI and international speaker. The programme connects current OpenAI capability to the architecture, evaluation and operating decisions required in real delivery.
About Arafat and Fedorai Questions
What teams usually ask before booking
How long is the OpenAI enterprise training?
The focused programme typically runs for two to three days. Duration depends on whether the priority is Codex enablement, API and agent development, or a connected programme covering both.
Does the training cover both Codex and the OpenAI API?
It can. The final agenda is scoped around the team. A development organisation may combine Codex workflows with API architecture, while another cohort may go deeper on only one surface.
Do participants need prior OpenAI experience?
No prior OpenAI platform experience is required, but the technical modules assume practical software development experience. We confirm prerequisites, languages and environment access before the workshop.
Can we use our own application or repository?
Private engagements can adapt exercises to your codebase, architecture or business scenario where access, confidentiality, data protection and workshop setup permit.
Does the programme include security and data controls?
Yes. Relevant modules can cover projects and access, application state, retention choices, data residency considerations, tool permissions, approval boundaries and evaluation evidence. The programme does not replace legal or compliance advice.
Is this certification or exam-preparation training?
No. The programme is designed around practical engineering capability, product delivery and enterprise operation rather than preparation for a certification exam.