AI for software development: autocomplete to agents

The first wave of AI in software was autocomplete: helpful, local, low-stakes. The current wave is different. Tools can now take on multi-step tasks, read a codebase, make a change across files, run tests, and report back.

What changed

Two things. Models got better at holding a large context and following a plan, and the tooling around them learned to take actions, not just emit text. The result is a shift from “suggest the next line” to “attempt the whole task, then let a human review the diff.”

Where it helps most

Code review assistance, writing and updating documentation, drafting tests, triaging incoming tickets, and turning a vague bug report into a reproducible case. These are the parts of engineering that are necessary, repetitive, and easy to review.

What stays human

Architecture, trade-offs, and anything where being subtly wrong is expensive. Agentic tools are fast, but speed without review is how small mistakes scale. The teams getting value treat AI output the way they treat a junior engineer’s pull request: useful, and always reviewed.

Getting started without the hype

Pick one workflow with a clear review gate, measure the time it saves, and expand from there. We help engineering teams set that up, train people on it, and keep it reliable as the tools change underneath.