Integration with AI: Advanced

Overview

At scale, integration stops being about a single API call and becomes about resilience: throughput, retries, ordering, observability, and recovery when something breaks at 2am. Adopting AI here means embedding it across the lifecycle, not bolting it on at the end.

This programme covers advanced integration patterns and a deep, practical way to adopt AI across architecture, build, testing, observability, and operations, moving you from building integrations to running an AI-adopted integration practice.

Who it is for

• Senior integration developers and solution architects
• Teams operating integrations in production at scale
• Leads who want AI adopted across design, delivery, and ops

What you will cover

1. Resilient, event-driven, high-volume patterns

   The architecture that holds up under load: async messaging, ordering, idempotency, and backpressure.

2. Spec driven integration design at scale

   Modelling contracts, failure modes, and SLAs in the spec, so the hard thinking happens before you build and AI has a precise target.

3. Building and refactoring complex integrations with agents

   Moving fast on intricate work against the spec, while keeping the control and review that production demands.

4. Observability and operations with AI

   Telemetry, anomaly spotting, and faster diagnosis, with token-efficient ops so issues are found and understood quickly.

5. Governance and team adoption

   Security, standards, correct model choice, token discipline, and adopting AI consistently across an integration team.

What you will leave with

• Resilient integration patterns for demanding environments
• An AI-adopted lifecycle from design through to operations
• Governance and standards to roll the practice out across a team
• A template repo and starter guide, so your team can begin the same spec-and-test-driven workflow at work