Agentic software development needs a new methodology

The software bottleneck has moved

As autonomous agents move into production systems, they change how enterprise software is built and maintained. Adoption decisions now require rethinking delivery, engineering practice, governance, and talent in parallel.

April's issue of Human in the Loop gets into the specifics: a new methodology for agentic development, agent-assisted legacy software maintenance, and the integration traps engineers keep falling into.

AI shifted the bottleneck. Agentic software development demands its own methodology.

The cost of software construction has collapsed. Code that once took weeks now takes hours, and with 73% of engineering teams using coding agents daily, the constraint on delivery has moved upstream to the definition phase. 

Traditional Agile was built for a world that no longer exists and the only real failure is moving too slowly. Stagnation has become the primary risk to enterprise delivery. Fast Impact Teams (FIT) is a framework designed to move engineering capacity away from documentation and toward architecture, product design, functional judgment, and AI orchestration. Learn about it in this article by Nahuel Vigna (co-founder & CEO at CloudX).

Agentic archaeology: the role of AI agents in legacy software maintenance

A substantial share of the world's most critical software is often decades old, undocumented, and known only to a handful of engineers. Keeping it alive, or transitioning it to a new team, has always been delicate, high-risk work that depended on talent that was hard to find. AI coding agents are compressing that ramp time significantly, mapping codebases, surfacing undocumented assumptions, and answering foundational questions before a line is changed.

The accumulated hands-on time a team has with a product is no longer the advantage it once was, but the human judgment required to modify trusted systems without introducing risk has not gone anywhere. This piece by Pablo Romeo (co-founder & CTO) examines where agents genuinely accelerate legacy work and where they should not be trusted (at least yet).

Straight from our devs

In-depth technical articles from our engineering team

From "the bench" to "ready to ship": how AI redefined my learning curve

Joaquin Islas (Sr Backend Engineer) shares a practical framework for using LLMs as paradigm translators to onboard onto unfamiliar stacks in days, with a worked example bridging Java Spring patterns to idiomatic Go. Read here.

What backend engineers get wrong about AI integration

Juan Manuel Altamirano (Sr Backend Engineer) describes eight production pitfalls to watch for in LLM integration: non-determinism, missing retries, token costs in loops, broken prompt caches, weak prompts, free-form parsing, fake RAG, and hallucinations. Read here.

Thank you for reading Human in the Loop. Stay tuned for more insights, stories, and practical guidance from the CloudX team. If you missed the previous issues, you can read them all here.