Amazon Web Services (AWS) has added support for the Model Context Protocol (MCP) with the Visual Studio Code and JetBrains plugins that are provided for Amazon Q Developer, a set of artificial intelligence (AI) agents that automate a range of software development tasks.
Originally developed by Anthropic, MCP support with these integrated development environments (IDEs) will make it simpler to invoke external tools that invoke some type of large language model (LLM).
Srini Iragavarapu, director of software development for Amazon Q Developer Coding Assistant, said this capability will make it easier to provide AI agents with additional context by providing access to additional data sources and application programming interfaces (APIs). Armed with those insights, it then becomes a lot simpler for developers to optimize the prompts they create to invoke Amazon Q Developer, he added.
That capability, in addition to creating more accurate code, will also make it simpler to integrate planning tools, create user interface components from designs and generate database documentation from actual schema without having to create additional custom integration code, noted Iragavarapu.
Previously, AWS only provided that capability via MCP support for the command line interface (CLI) option it makes available for Amazon Q Developer.
Over time, multiple classes of AI agents will emerge that provide different levels of reasoning capabilities that will be assigned tasks based on the level of complexity involved, said Iragavarapu. AWS also continues to evaluate the agent-to-agent (A2A) protocol put forward by Google to make it simpler for AI agents to communicate over a standard set of interfaces, noted Iragavarapu.
It’s not clear how widely AI coding tools have been adopted a Futurum Research survey noted, but found 41% of respondents expect generative AI tools and platforms will be used to generate, review and test code.
There is, of course, already no shortage of AI coding tools, so the pace at which code is being developed has already begun to accelerate. Exactly how much of that code is making it into production environments is unknown. However, the next major challenge will be incorporating AI agents into the software engineering workflows used to deploy applications. Many of the DevOps pipelines relied on today to deploy software were not designed to handle the volume of code being generated by AI tools. The expectation is that many of the brittle scripts that DevOps engineers rely on today to create pipelines will eventually be replaced by AI agents that can perform tasks at higher levels of scale.
In the meantime, each organization will need to determine to what degree they may want to standardize on a specific AI tool versus letting each developer select one based on their personal preference. The important thing is to remind developers to review the code created by AI coding tools that, on occasion, are still prone to hallucinations. After all, even if a machine wrote the code, it’s still a human developer who is ultimately responsible for the quality of the code being created.
