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Read more: https://www.speedscale.com/blog/test-ai-code-with-traffic-replay/

A breakdown of the three potential ways software engineers will interact with AI coding assistants, ranging from local desktop setups to fully automated software delivery factories. Learn more: speedscale.com.

While AI coding tools dramatically slash development time, they are quietly inflating testing and maintenance burdens because teams no longer fully understand their codebase. Discover how leading engineering teams are shifting focus to testing and context packages to eliminate bottlenecks and unlock true AI efficiency.

AI agents can write tests, but those tests can repeat the same assumptions that led to the code. In this walkthrough, I use Cursor and proxymock to replay real application traffic against a local Go app, compare requests and responses, and validate code changes with real data.

Mocking for testing starts off easy, but once you scale to multiple teams and AI agents, handcrafted mocks become a serious form of technical liability. Instead of treating mocking as an individual software engineering task, shift your mindset to treat it as a platform engineering task focused on automation and continuously refreshed modern data. Watch to see how adopting technologies like traffic replay to simulate realistic backend sandboxes can transform your modern testing workflow!

As companies adopt AI coding tools, code review processes are breaking down. Traditional compliance methods are slowing teams down, but human engineers aren't going to spend hours reading AI-generated low-level code forever. How will SOC 2 adapt to the era of AI-driven development? Drop your thoughts in the comments and subscribe for more tech insights! Learn more: speedscale.com.

Read more: https://speedscale.com/blog/your-observability-stack-found-the-fire/

Learn more: speedscale.com.

Learn more: speedscale.com.

Learn more: speedscale.com.