Zencoder today emerged from stealth to make generally available an artificial intelligence (AI) platform that in addition to generating code is also able to repair code, create tests and optimize code in real time using built-in static analysis capabilities.

Company CEO Andrew Filev said the company’s namesakes platform makes use of multiple proprietary and open-source large language models (LLMs) to train agents to handle each of those tasks. That approach will make it simpler for Zencoder to extend the capabilities of its platform across the entire software development lifecycle (SDLC), he said.

The first generation of AI coding assistants in comparison don’t provide a comprehensive approach and are more prone to hallucinate in ways that generate flawed code, noted Filev. Zencoder solves that issue by providing development teams with a set of AI agents that can apply static analysis to code as it is being developed, he added.

For example, a Repo Grokking agent analyzes the entire code repository to provide the appropriate level of context required to generate code before an Agentic Repair agent creates a pipeline that automatically analyzes, fixes and refines generated code.

An Agentic Loop agent then adds a planning and feedback loop to further fine-tune the AI models that Zencoder provided.

Additionally, Zencoder also allows DevOps teams to define their custom agents to automate additional tasks.

Collectively, these AI agents address tasks such as bug fixing, refactoring, new feature development and the writing of unit tests in ways that make application development teams truly more productive, in a way that also ensures higher-quality code is produced, said Filev.

Zencoder is, in effect, using multiple AI agents in a collaborative fashion that enables them to review the work produced by another AI agent. The challenge now will be integrating those AI agents into DevOps workflows that still require software engineers to manage them.

Many DevOps teams are already making extensive use of AI. A Techstrong Research survey finds a third (33%) of organizations are making use of AI to build software, while another 42% are considering it. However, only 9% have fully integrated AI into their DevOps pipelines, while another 22% have partially achieved that goal.

A separate DORA report from Google also suggests that AI has yet to substantially improve the rate at which software is actually being deployed.

At this juncture, it’s still largely a question of when rather than if AI will improve both the quality and rate at which software is developed. However, there are no magic bullets. DevOps teams need to thoughtfully harness the capabilities of AI platforms to ensure objectives are achieved. Otherwise, most of the AI effort being made will be left in the hands of developers, who, as they experiment with AI, are likely in their haste to trust the output generated too much.

In the meantime, DevOps in the age of AI is never going to be the same. DevOps teams need to not only embrace what AI can do today but also prepare for advances that will soon be coming at a furious rate.