DNAi Integration Professional Certification

Design multi-system AI integrations that survive contact with production traffic, real auth, and partner rate limits. You learn to wire models into the systems your teams already depend on without becoming a single point of failure.

• Start From the System Map, Not the Model
• Wrap Every Model Behind a Contract
• Orchestration Patterns That Fail Safely
• Idempotency and Exactly-Once Writes Across Retries

Make your AI integrations observable, controllable, and reversible before they go wide. This is the difference between an outage you fix in ten minutes and one that erodes trust for a quarter.

• Instrument Before You Scale
• Guardrails That Protect Without Strangling
• Rollback You Have Actually Tested
• Ownership and the On-Call Reality

An integration that no one trusts is a failed integration, no matter how clean the code is. This module is about landing AI in a way that protects morale and earns durable adoption.

• Co-Design With the People Doing the Work
• Augmentation People Actually Believe
• Pilots That Produce Evidence
• Reading and Working With Resistance

Rolling AI across many teams is a sequencing problem, not a copy-paste problem. This module covers how to order, pace, and stage a multi-team program so each wave succeeds and feeds the next.

• Sequence by Readiness and Reversibility
• Build a Platform, Not Ten Snowflakes
• Pace So Trust and Support Keep Up
• Manage Shared Dependencies and Contention

Integration does not end at launch; the real work is keeping a multi-team AI program trustworthy as models drift, teams change, and edge cases pile up. This module covers the operating rhythm that keeps the whole thing healthy.

• An Operating Cadence That Catches Drift
• Managing Model and Provider Change
• Keeping Humans Skilled and Knowledge Alive
• Expand, Hold, or Retire

An AI integration is a new data pathway through your systems, and that pathway carries security, privacy, and regulatory obligations the model does not absolve you of. This module makes you the owner who can put an integration in front of a security review, a privacy officer, and an auditor and have it hold up.

• The Prompt Is a Data Flow, Not Just Text
• Defending the Boundary: Prompt Injection and Untrusted Content
• Auditability, Retention, and Explaining a Decision Later

A multi-team AI program lives or dies on economics and resilience as much as on quality. This module makes you the owner who can defend the program's unit economics to finance, keep it within capacity under load, and survive the day a single vendor fails or changes the deal.

• Unit Economics Beyond the Per-Token Price
• Capacity, Degradation, and Backpressure Under Load
• Vendor Concentration, Portability, and Build-Versus-Buy

Coding agents like Claude Code and ChatGPT Codex do not just suggest code - they plan, edit files, run commands, and iterate. Learn what each one is and what it can build.

• What a coding agent is
• Claude Code
• ChatGPT Codex
• Gemini and Antigravity

Get Claude Code, ChatGPT Codex, and Gemini running inside VS Code or Antigravity, and understand plugins, extensions, and MCP - the connectors that let an agent reach your tools and data.

• Install Claude Code
• Install ChatGPT Codex
• Gemini in VS Code and Antigravity
• Plugins, extensions, and MCP

Skills are packaged bundles of instructions, knowledge, and tools that teach an agent to do something well. Learn to find, vet, and install GitHub repo skills into Claude Code and Codex.

• What a skill is
• Find and vet repo skills
• Install skills into Claude Code
• Add skills to Codex and verify