Nearly a year into the agentic coding explosion, many teams now use AI to write software as their de facto approach. As a result, Anthropic’s engineers are shipping eight times as much code as before, and some engineers are shipping thousands of PRs per month.

Management teams expecting their engineering capacity to explode will find that AI-written code doesn’t necessarily compress the software development lifecycle (SDLC) on its own.

The critical bottleneck in software creation has shifted from writing code to a later SDLC stage. After coding agents spit out new code, it still needs testing, and traditionally that meant humans writing the tests. That work includes deciding which user flows need testing, scripting tests for them, and rejiggering them when those flows change.

We’ve made it easy to write code and, in the process, increased the burden on humans to test new code. If we want to slash software development timelines, something has to change.

The observability and monitoring giant Datadog bought Propolis earlier this year, adding agent-written, goal- and output-oriented test writing to its platform. The approach lets agents explore applications, follow user flows on their own, and generate tests from plain-language prompts.

Add Datadog’s observability heft, and you get a system that can test code while providing the logs and traces it needs to vet system performance without the fragility hardcoded testing brings.

Which best describes where you are with AI-generated or automated testing? *

m. m. Eastern.

Source link