env zero (formerly env0) today revealed it has revamped its infrastructure automation platform to add artificial intelligence (AI) capabilities, including beginning next month a Static Code Analyzer Agent to identify and troubleshoot issues.
Chris Graham, chief marketing officer for env zero, said this iteration of the company’s Cloud Governance Platform will provide a control plane for automating and governing infrastructure-as-code that now takes advantage of AI to better connect repositories and cloud computing platforms using a Model Context Protocol (MCP) server. Originally developed by Anthropic, MCP is emerging as a de facto standard for exposing data to AI applications and agents.
The core capability is also making it possible to build a Static Code Analyzer Agent that detects who is accessing services, the level of compliance, data protection, security and protection of secrets that is being achieved and maintained, which can then be remediated via a pull request that is generated via a single click using the Cloud Governance Platform.
Compatible with Terraform, OpenTofu, Pulumi, CloudFormation, Terragrunt and Kubernetes tools, the Cloud Governance Platform is now making it simpler for AI agents to be used to programmatically provision cloud infrastructure in a way that enables policies to be enforced as code, said Graham.
The ultimate goal is to dramatically increase the pace at which IT infrastructure can be safely provisioned using the context that can be uniquely provided by the Cloud Governance Platform developed by env zero without requiring DevOps teams to master a completely separate user interface, he added.
It’s not clear to what degree organizations are embracing IT infrastructure automation platforms, but the rise of AI agents might soon force the issue. AI agents, while improving productivity, will need a set of guardrails to be in place that prevent them from exceeding the scope of the task that they have been assigned. Otherwise, it becomes probable that sensitive data will be exposed to either an AI agent or the application development team employing them in a way that runs afoul of any number of compliance requirements.
Ultimately, AI agents invoking MCP servers should also reduce the number of scripts and application programming interfaces (APIs) that DevOps teams currently need to support. As the overall complexity of DevOps workflows is reduced, it then becomes feasible to provision more infrastructure at scale at a time when AI is also dramatically improving the pace at which applications are being developed.
Each DevOps team will need to determine to what degree to rely on AI agents to automate tasks, but, for now at least, those teams would be well advised to review any code created by an AI agent. In the meantime, DevOps engineers would be well-advised to start identifying more tasks than can be potentially performed by AI agents as part of a larger effort to reduce the total amount of manual toil that, in addition to burning out teams, also creates more bottlenecks that slow down the pace of application development more than anyone cares to admit.
