AI coding agents are making it easier than ever to produce software. Ensuring that software is secure before deployment is another matter — one that AWS thinks AI should help with too.
As enterprises adopt agentic development workflows, the volume of first-party code being created and modified is rising rapidly. Yet the process of validating vulnerabilities, determining whether they are exploitable, and fixing them often still depends on developers and security teams working through findings manually.
AWS is aiming to address that imbalance with Continuum, a new service designed to continuously discover, investigate, and remediate vulnerabilities in enterprise environments, whether the code is their own or from third parties.
Rather than simply generating alerts, the service is intended to help enterprises move findings through the entire remediation lifecycle, AWS VP of Security and Observability Chet Kapoor wrote in a blog post.
For first-party applications, Continuum can analyze cod
AI coding agents can tend to isolate research, running experiments and generating ideas that are then forgotten when context windows reset. This can waste tokens, as models then repeat the same mistakes and hit the same dead ends.
But new research argues that it’s not the model itself, but the overarching ‘tree,’ that needs tweaking. To that end, data scientists from the Gaoling School of Artificial Intelligence, Renmin University of China, and Microsoft Research have introduced Arbor, a “persistent hypothesis tree” that helps agents remember and refine learnings over long research sessions.
A long-lived coordinator manages research strategy across the tree, while short-lived executors spin up isolated worktrees to test different hypotheses. As results come back, the tree updates, narrowing and refining throughout experimentation.
In practical tests, this technique delivered more than two-fold performance gains over standard AI coding agents across real-world engineering tasks, for the
AWS turned on AI traffic monetization inside AWS WAF on Monday, letting any site behind Amazon CloudFront charge AI agents per request in USDC through Coinbase's x402 protocol. It is the first time a hyperscale cloud has wired onchain settlement into its content-delivery edge.
For many developers, the hard part of building an AI application isn’t the model anymore. It’s keeping the application’s knowledge current.
Retrieval-augmented generation (RAG) has become a popular technique for grounding AI applications in enterprise data, but it also introduces a steady stream of operational work, including tasks such as updating embeddings and indexes, synchronizing data sources, and tuning retrieval performance.
AWS is seeking to remove much of that burden with Bedrock Managed Knowledge Base, a new managed service that automates the retrieval layer behind enterprise AI applications.
“By default, the service automatically selects and manages a default embeddings model, re-ranker model, and foundational model on your behalf, so you can get up to speed quickly without needing to pick or maintain one yourself,” Daniel Abib, senior solutions architect at AWS, wrote in a blog post.
In order to help maintain data pipelines without building and managing custom integrations
It is tempting to date cloud computing from the launch of Amazon S3 in 2006 and the rise of infrastructure as a service (IaaS) that followed. That was certainly the moment the market changed in a visible, irreversible way. But the truth is that cloud began earlier, in the 1990s, when software as a service (SaaS), application hosting, managed services providers, and various forms of remote subscription computing started to reshape how enterprises thought about owning and operating technology. Even then, the core value proposition was familiar: Let someone else run the infrastructure, abstract the complexity, deliver capability as a service, and allow the business to consume only what it needs.
What AWS changed was the scale, accessibility, and precision of the execution. Amazon turned infrastructure into a programmable utility. It made compute and storage available in ways that were elastic, self-service, API-driven, and globally reachable. That was the breakthrough. Enterprises had out
AI coding agents are becoming critical to software development, but the configuration files that guide them, such as Agents.md or Claude.md, can be “smelly.”
That means they can contain structural flaws, redundancies, or counterproductive workflows that bloat context, waste tokens, and make coding agents less reliable.
Researchers from the Department of Computer Science at Brazil’s Federal University of Minas Gerais hope to shed light on this problem, presenting what they call the “first catalog of smells” for coding agent configuration files. The most odorous? Lint and skill leakage, context bloat, and conflicting instructions.
“Our results show that these smells are widespread in practice,” the researchers wrote. Consequently, they “may directly influence how coding agents interpret project conventions, prioritize instructions, and perform development tasks.”
Smelly configs in the harness make models misbehave
Agents like Claude Code, Codex, Cursor, and Gemini are increasingly taking
Expanded collaboration enables customers to adopt and scale agentic AI as they modernize and run mission‑critical workloads on AWS NEW YORK, June 18, 2026 — Kyndryl, a leading provider of […]
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The EU probe could reshape cloud market dynamics, potentially enhancing competition and impacting AWS and Azure's strategic operations.
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