Flex Gateway Error Handler Policy Demo and PDK Context Engineering Introduction

• 0:00 - Introduction & Demo Setup: The video begins with a quick demo of a global error response policy for MuleSoft Flex Gateway. The API instance, called "echo API," is deployed on a local Flex Gateway and forwards requests to Postman Echo. The goal is to configure a policy in API Manager to handle errors globally with a similar format. • 0:45 - Configuring & Demoing 400 Error Policy: A custom inbound policy, published to Anypoint Exchange, is added to the API instance. The configuration involves "error mapping" where you specify the HTTP code to intercept, such as 400. Policy fields are defined using DataWeave, allowing dynamic values from request or response payloads, headers, or body. After applying the policy, a 400 request demonstrates how the original response is intercepted and re-formatted with new fields. • 1:30 - Configuring & Demoing 500 Error Policy with Problem JSON: The video shows how to add another mapping to the policy to intercept 500 responses. It demonstrates configuring the response format as Problem JSON, which is a common standard for global error handlers and part of RFC specification. • 2:15 - Demonstrating Multiple MIME Type Support (XML): The policy supports multiple MIME types for each intercepted error code, including JSON, XML, or Problem JSON. The demo shows switching the 500 error response from Problem JSON to XML format. • 3:00 - Introduction to Flex Gateway Policy Development Kit (PDK): The speaker transitions to explain how the policy was developed using the Flex Gateway PDK (Policy Development Kit). The PDK is an SDK with detailed documentation and prerequisites for developing policies. It involves installing dependencies, bootstrapping a project using a CLI, and working with a specific project structure including Makefile, GCL files (for policy interface definition), and Rust for the policy's logic. Rust and Cargo (similar to npm) are used for development and testing. • 4:30 - AI-Powered Context Engineering Approach: The policy's code was primarily developed using a "context engineering" approach with an AI agent named "Cloud". The process involves creating "product request prompts" (PRPs) that describe the desired feature in detail. The Cloud CLI can generate these PRPs, which also serve as documentation and architectural descriptions for the project. • 6:00 - Crucial Local Testing Setup & AI Self-Correction: A critical aspect of this context engineering process is having a working local testing setup with Docker Compose. The speaker developed a script that allows Cloud to run integration tests, which spin up a local Flex Gateway for each test with mocked APIs. A key insight was enabling the AI to see the logs of the Docker container during tests, which allowed it to analyze why code wasn't working and self-correct errors. The AI even wrote the bash script to view the logs itself. • 7:30 - Conclusion & Code Availability: The efficiency gained from this approach meant "developing and testing the policies was just a matter of time". All the code, including scripts and tests, is publicly available on GitHub for others to use, fork, or extend.