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Harness Extends DevOps Reach to the Realm of AI Agents

Harness today extended the reach of its core continuous integration/continuous delivery (CI/CD) platform to enable DevOps teams to build and deploy artificial intelligence (AI) agents. At the core of that capability are five additions to the Harness portfolio, including an AI Asset Catalog that automatically discovers every agent, skill, and plugin that exists within repositories and, just as importantly, who within the organization owns them. There is also a Harness AI Evals tool that measures the quality of an AI agent by creating gates that automatically catch regressions whenever an agent or model changes. Additionally, an Agent Deployments offering extends the canary releases, approvals, and Open Policy Agent (OPA) guardrails that Harness already applies to Kubernetes deployments to managed agent runtimes like Amazon Bedrock AgentCore and Google’s Agent Runtime. AI Configs, meanwhile, provides access to a feature management tool to manage prompts and model changes at runtime. ...
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AI Moved the Bottleneck From Writing Code to Understanding and Trusting It

AI code generation has delivered on its first promise. Teams are shipping more, covering more test cases, and moving faster than they could have imagined just a year ago. The productivity gains are real and the organizations that have leaned into AI adoption have proof points to show for it. That first wave of wins is also creating a second wave of questions, and the engineering leaders who are building influence now are the ones who saw those questions coming. The Quick Wins are Real; So is What Follows Most teams start using AI code generation in low-risk, repetitive work: Documentation, unit testing, and simple functions. Data from recent research bears this out, and the productivity gains in these areas are real. The problem is what happens at scale. As AI generates more code, it creates more to review, more to maintain, and more places for issues to hide. Review load compounds. Pull requests change in size, frequency, and composition. The code looks different than it used to,...
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Ten Great DevOps Job Opportunities

DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience. Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers. Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others. The ten job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered. We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration. Greenhouse Tines Remote, US Senior Site Reliability Engineer – Government Cloud $210,000 to $220,000 Axon New York, NY Senior Site Reliability Engineer, Axon 911 $141,000 to $216,600 Rew...
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HashiCorp Introduces tfpolicy, a Native Policy Framework for Terraform

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...
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AI-Generated Code Is Cheap But the Context Infrastructure Behind It Is Not

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...
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Why You Need AI Agent Security Validation in Software Testing

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...
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Anaconda Acquires Kilo Code to Unify AI Development from First Prompt to Production

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...
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