In our recent blog “Top Tech Trends for 2023. Part 1”, we predicted that AI would reach new heights in the coming year. AI already touches many areas of our lives, and it’ll continue to do so. However, one particular AI development is set to have a significant impact on the work of IT professionals.
AI assisted coding is a practice that uses Artificial Intelligence to help generate code. As such, it will mainly benefit software developers, however it also holds the potential to help ITOps engineers too.
As we head further into 2023, ITOps teams will use AI powered coding tools to streamline their work and reduce the risk of error.
ITOps and AI assisted coding tools.
Here are some ways in which ITOps engineers can benefit from AI-assisted code generation tools and platforms.
Infrastructure-as-Code templates.
Infrastructure-as-Code tools are useful for configuring large numbers of servers or cloud hosting environments. ITOps engineers can use IaC to avoid having to configure each resource by hand. Instead, they can save time and reduce the risk of error by writing code to define how the resources should be configured, then applying these configurations automatically.
In the future, configuring IaC files may no longer require a manual touch. Instead, based on natural-language input from ITOps engineers about how infrastructure should be configured, AI-powered coding tools could write the files automatically.
Policy-as-Code Rules.
Policy-as-code is an approach to setting up IT resources using code-based configuration files.
As with IaC, ITOps teams could use a similar method to write policy-as-code files.
Previously, someone has had to write policy-as-code rules by hand. Looking forward, AI-assisted coding would let ITOps teams generate policy-as-code rules automatically.
This user-friendly solution might encourage further adoption of policy-as-code approaches to IT operations management. Currently, the use of code-based configuration engines remains primarily limited to IaC. If it were possible to generate all types of policy configurations automatically, it would become easier to configure any type of IT resource using this approach.
Alert Management.
ITOps teams may also benefit from AI-assisted coding when it comes to Alert Management.
Traditionally, engineers have had to write rules that determine when a monitoring tool should generate an alert and how interrelated alerts should be grouped together.
Instead, engineers could describe how alerts should be managed, and the AI-powered code generation tools would write code to automate the alerts accordingly.
Configuring Observability tools.
Observability tools could also be configured using code generated by AI-powered tools. ITOps engineers would describe the metrics they want to collect and the data sources they want to monitor. AI would then write configuration files for their observability platforms based on those descriptions.
AI Assisted Coding Tools and ITOps: The challenges.
Using AI assisted coding tools for ITOps is not without its challenges though. Here are a few of the challenges for using AI assisted coding tools within the domain of ITOps.
Today’s AI assisted coding tools such as GitHub Copilot and Visual Studio IntelliCode, are generally better at writing software code than at writing configuration code. This makes them more suitable for software development than ITOps.
Many ITOps platforms that could benefit most from AI-assisted coding don’t support the integrations necessary to enable AI-powered configurations. Vendors that target the ITOps space will need to embrace AI-assisted code generation for this to change.
It seems that if we were able to address the shortfalls, AI-assisted coding could offer tangible benefits to IT operations teams as well as to software development teams.
Interested in software engineering?
For more, why not read ‘What is the future for Software Engineering?’
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