In the tricky business of choosing the right artificial intelligence coding models for security and cost, development teams need to keep in mind two important factors. First, even the best and best-known AI coding assistants vary widely in performance, both between and within individual frameworks. Each has strengths and weaknesses, and there is no across-the-board best of the bunch; a model that leads the pack in one framework may be in last place in another. And second, when it comes to generating secure code, you don’t necessarily get what you pay for. The real costs of using AI agents can mount quickly depending on an agent’s use of tokens, and some models are more inclined than others to make extensive use of them, but there’s no correlation between cost and the level of security provided. Those are among the findings of an in-depth study conducted by Secure Code Warrior and the Royal Melbourne Institute of Technology (RMIT) to evaluate the security behavior of leading AI codin...
The most expensive mistake teams are making with AI agents right now is treating them as microservices with a language model bolted on. The approach is understandable; microservices are the architecture most platform engineers know best, agents feel like services that happen to think, and the migration path looks familiar. But the analogy only holds long enough to lead you somewhere painful. Microservices changed how software gets built. The idea was to help teams work independently, ship faster and scale efficiently. In those days, shipping software meant one giant codebase compiled together, tested together and released on 12- or 18-month cycles. Then the cloud came along, and teams needed to ship in weeks or days. The only way to do it was to split systems apart and let each piece move on its own schedule. It worked, but the systems underneath stayed simple in one crucial way: they were deterministic. Distributed systems are hard, but the behavior was predictable. A service receiv...