News and Tech Update

HashiCorp has introduced Terraform policy (tfpolicy), a new policy-as-code framework built directly into Terraform. Now available in public beta on HCP Terraform, tfpolicy lets platform teams write governance rules in HCL, the same language they already use to define infrastructure, rather than reaching for a separate policy tool and a separate syntax. The launch addresses a problem that’s grown alongside Terraform itself. As infrastructure estates expand across hybrid cloud environments, platform teams are managing systems spanning public cloud providers, SaaS platforms, on-premises infrastructure, and internal platforms, often with multiple teams contributing changes via Terraform. That sprawl has pushed teams toward multiple tools and disconnected policy workflows just to maintain consistent governance. Why the Old Model is Straining Policy as code isn’t new. HashiCorp’s own Sentinel and the open-source Open Policy Agent (OPA) have handled this job for years, ena...

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...

Anaconda Inc. has acquired Kilo Code, an open-source, model-agnostic platform that embeds artificial intelligence (AI) agents directly into developer workflows. The acquisition integrates Kilo’s rapid-growth community of over three million developers into Anaconda’s expanding enterprise AI-native development ecosystem. The transaction positions Anaconda to secure the entire software development lifecycle, from the developer’s initial prompt to final enterprise deployment. Financial terms were not disclosed. The deal represents a major strategic step in Anaconda’s bid to address a critical industry bottleneck: The estimated 80% of enterprise AI projects that fail to reach production. By combining Kilo’s front-end AI agents with Anaconda’s secure package distribution and its recently acquired Outerbounds orchestration platform, Anaconda aims to create a highly secure, continuous pipeline for AI-native software development. Kilo’s platform is built for agentic engineering, where...

SmartBear this week revealed it is narrowing the divide between coding and testing by integrating its platform with artificial intelligence (AI) coding tools . Sheryl Koenigsberg, senior vice president of product marketing at SmartBear, said integrations with AI coding tools from Anthropic, Atlassian, GitHub, and Amazon Web Services (AWS) will make it simpler for application developers to test code without having to exit the tools they use to create code. Specific capabilities added to the SmartBear portfolio include a SmartBear Model Context Protocol (MCP) server for GitHub and connectors to ReadyAPI and Swagger for developers using Claude Code from Anthropic. The overall goal is to reduce the level of friction that application developers might encounter as the volume of code being generated using AI coding tools continues to exponentially increase, said Koenigsberg. In the absence of that testing, the amount of technical debt that software engineering teams are accruing will soon ...

The Kilo Code acquisition complements Anaconda’s Python roots, but it also reveals a larger ambition: Moving up the AI stack before the value—and the developer relationship—moves beyond it. Anaconda announced this week that it is acquiring Kilo Code, and the announcement arrived wrapped in enough AI marketing language to fill several context windows. There is a “tokenpocalypse.” Enterprises are “token-maxxing.” CIOs are being asked whether they know where their data is. Anaconda and Kilo, meanwhile, are promising “AI on your own terms.” Let’s strip all of that away. The deal is important enough without the promotional wrapping. Kilo Code is an open-source, model-agnostic coding agent used by more than 3 million developers. According to the companies, it orchestrates nearly 10 trillion tokens per month and can route work across more than 500 models. It operates inside VS Code, JetBrains and the command line, placing it directly where developers and AI agents increasingly perform the...