In the rapidly developing world of AI, MuleSoft is evolving its integration, API management, and AI capabilities. Although existing code generation models have greatly improved efficiency in many industries, they are not ideal for generating code in languages specific to the MuleSoft ecosystem, like DataWeave, RAML, and OAS.
In this blog post, we will explore MuleSoft’s synergy with AI. We’ll also highlight the benefits and limitations of existing models like CodeX and CodeGen, and how an internal, MuleSoft-focused AI model can address these challenges.
The challenge of code generation
AI-driven, open-source code generation models like CodeX and CodeGen have proven their effectiveness with popular programming languages, such as Java and Python. However, they’re ineffective when dealing with languages like Go, Julia, or domain-specific languages (DSLs) that are used within the MuleSoft ecosystem. DataWeave, RAML, OAS, and other MuleSoft-specific languages require a different approach, which general-purpose AI models may struggle to provide. Also, there are several areas where MuleSoft Developers may need help.
Let’s identify some of the most common issues that developers have when getting started and integrating with MuleSoft, and then see how AI can assist.
Common developer pain points
Many developers find it challenging to start with MuleSoft due to its specialized nature. They are often unaware of API design languages, like RAML and OAS, and it can be difficult to build a perfect API following best practices and standards. The Mule palette consists of several connectors and components, and it can be overwhelming for a new developer to decide which component/connector should be used and when to use it.
Some Dataweave transformations can be complex, involving nested payloads and intricate mappings.
Debugging and testing
If an integration ecosystem is vast, involving several end systems and APIs, then debugging Mule applications can be time-consuming. Although we have a Test recorder to automate test cases, it is a complex task to validate the unit test cases with MUnits and aim for maximum test coverage.
Developers and architects often need help choosing the right integration pattern and connectors, as well as optimizing the solution.
Writing documentation for API specification and Mule projects can be time-consuming. It’s also essential to maintain and update this documentation to align with changes in APIs and Mule applications.
Solving the challenges with MuleSoft’s own LLM
Third-party AI models, like GPT 3.5 and GPT 4, allow you to have human-like conversations when interacting with a chatbot. You get answers to your questions, and the chatbot also performs tasks on your behalf, like writing an essay or email, and so on.
These models are trained for thousands of downstream tasks, but they are not tailored for the MuleSoft ecosystem. These third-party models rely on static datasets that can become outdated, leading to incorrect data generation. That’s where MuleSoft’s AI innovation stands out.
With MuleSoft’s training dataset, we can continuously retrain such models with the latest MuleSoft data, allowing us to stay up to date with evolving MuleSoft versions. Hence, we can remove obsolete data and focus on the latest data.
We prioritize depth over breadth, and we are constantly updating our training dataset to avoid obsolete data generation. For example: Mule 3 training data, which is old and obsolete, will be removed, and we will keep Mule 4 training data to avoid bad data generation. In addition, this training dataset abides by trust and security compliance, thereby adhering to Salesforce’s values.
Let’s now review what’s in the pipeline with AI and MuleSoft to accelerate integration development.
Other MuleSoft + AI capabilities
Einstein for Anypoint Code Builder: Pilot Oct ’23
For MuleSoft’s newest cloud- and desktop-based Studio, Anypoint Code Builder, we’re introducing Einstein for Anypoint Code Builder: Generative Flows. This will help you convert natural language into flow and code snippets, thereby reducing development time.
Intelligent Document Processing (IDP): Pilot Q4 ’23
IDP will help you with seamless automation to extract and interpret data from PDFs and documentation accurately.
API Management for AI: (Release TBD)
With API Management for AI, you can create custom policies for your API applications that use LLMs. You can also create custom policies quickly with generative AI for specific use cases.
In the realm of MuleSoft and AI, combining the power of MuleSoft’s ecosystem knowledge with a dedicated, internal AI model is the recipe for success. By prioritizing depth over breadth, and staying up to date with the latest MuleSoft developments, we can provide you with an exceptional tool for code generation, debugging, testing, and support.
This solution accelerates your MuleSoft journey with AI and unlocks new possibilities for seamless integration and application development.
- MuleSoft + AI blog posts
- Trail: Be an AI-Minded Admin
- Webinar: Connect and automate AI + Data + CRM across any system
About the author
Akshata Sawant is a Senior Developer Advocate at Salesforce. She is an author, blogger, and speaker, and the co-author of the title, MuleSoft for Salesforce Developers. Akshata is an active member of the MuleSoft Community and a former MuleSoft Ambassador. She loves reading, dancing, traveling, and photography, and is a big-time foodie. Follow her on X (formerly Twitter) and LinkedIn.