How software architects and project managers can leverage agentic AI – cio.com

You may have heard of vibe coding. But what about vibe software architecture design, or vibe project and technical management?
I’m here to tell you that teams can “vibe” these aspects of software development, too. Using AI agents, software architects, project managers and technical managers can accelerate their workflows, just as developers are now using AI to help with code implementation and integration processes.
Allow me to explain by detailing how the teams I work with are already putting agentic AI technology to work in areas like software architecture and project management.
Agentic AI is the use of software agents — meaning programs that can carry out tasks autonomously, with guidance from AI models — to automate complex tasks.
Starting about a year ago, software developers began cluing into the benefits of agentic AI as a way of streamlining tasks like writing and testing code.
And now, other stakeholders within the software development process are starting to realize that they can take advantage of AI agents, too. For example:
To be clear, I’m absolutely not suggesting that AI agents can replace humans in these roles. Software development teams will still need architects, project managers and technical managers for the foreseeable future. But by using agents to kick off and automate workflows, these stakeholders can work faster and at an increased level of scale. For example, it’s reasonable to expect AI to succeed in generating a software architecture that is 80 percent complete and accurate, significantly reducing the time that a software architect has to spend reviewing and updating plans manually.
As is the case when using AI agents to help with coding, software architects, project managers and technical managers should expect to run into some special challenges when integrating agentic AI into their workflows.
One is that AI can make inaccurate assumptions, especially when the humans who guide it don’t include complete details within prompts. If the result of work completed by AI agents is inaccurate, architects or managers will need to tweak their prompts and try again. Indeed, iteration is key to getting AI agents to produce efficient, reliable designs and plans.
A second challenge — and one that is impossible to solve through simple iteration — is agents’ lack of awareness of bureaucratic obstacles that may apply to a particular organization. For example, an agent might assume that it can modify DNS records automatically. But at some companies, DNS changes require a human to issue a formal request, which must, in turn, receive review and approval. This is an example of why humans must review AI-generated software architectures and project plans and update them based on context that AI doesn’t know about.
As I mentioned, my organization is already using AI agents to help with software designs, project management and technical management. Here are two recent examples of initiatives we completed with help from agentic AI:
Agentic AI remains a fast-changing domain, and how teams leverage AI agents will no doubt continue to evolve. I think of the examples I’ve mentioned above as “proofs of concept” showing that AI agents have an important role to play beyond coding, rather than the be-all, end-all of what agentic AI will do when applied to multiple facets of the software development process.
Nonetheless, the results we’ve already achieved by leveraging AI agents to help create software architectures and manage software projects highlight the importance of thinking holistically about how technical teams leverage agentic AI. The headlines still tend to focus on vibe coding, but clearly, agentic AI is not just for coders.

This article is published as part of the Foundry Expert Contributor Network.
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Derek Ashmore is AI enablement principal at Asperitas, where his focus is on DevSecOps, infrastructure code, cloud computing, containerization, making applications cloud-native and migrating applications to the cloud. His books include the “The Java EE Architect’s Handbook” and “Microservices for Java EE Architects.”
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