The cost curve for generating code with AI has moved in one direction, and it has moved fast. What used to require a senior engineer’s full attention for an afternoon can now be scaffolded in minutes, and the price per token for the models doing it keeps falling. By the metric that dominated every early AI productivity argument, the cost of generation, the economics are undeniably favorable and getting more so. But production deployments have surfaced a different cost that the token price does not capture and that the demo environment was never designed to reveal. The cost of giving the model the right context to generate something that is actually correct, maintainable, and safe to ship is not falling. In many organizations it is rising, and the engineering infrastructure required to manage it reliably does not exist yet in most teams that need it. Context engineering has become one of the more discussed terms in AI infrastructure circles, and the discussion has accumula...
Engineering teams have been racing for the last two years to deploy AI agents that can find bugs faster than any QA team ever could. Autonomous testing agents can crawl through codebases, identify vulnerabilities, and generate test coverage reports while developers finally get to take a breath. The irony is that while development teams enjoy that brief reprieve, the workload for testers and security researchers has exploded, because now they have to validate not just the code but the agents doing the testing. And most leaders are now facing a disturbing truth that should fundamentally change how we think about quality engineering. The agentic testing platforms we trusted to secure our applications may themselves be introducing attack vectors we have never seen before, says Ahmed Zaidi , Chief Executive Officer of Accelirate , who leads the companyʼs automation and AI-driven testing strategy. Grappling with a fundamental challenge that most QA leaders have not yet confronted, he exp...