The New Stack

AWS Emulator proves agents need a verification loop. Realism is the hard part. sponsor-signadot,sponsored-post-contributed, Imkara Visual for Unsplash+ This week, Amazon Web Services released AWS Emulator, a local copy of AWS that coding agents can test against. It covers more than 200 AWS services and was written entirely by agents. The most useful part of the launch is a story Swami Sivasubramanian tells in the announcement.

Claude Fable 5.2 vs. Fable 5.1: Fewer refusals, with small savings on the login fix Photo by New Material on Unsplash Anthropic’s Claude Fable 5.2 promises fewer refusals and lower token prices, but those changes played out very differently in our head-to-head coding comparisons. Anthropic compared Fable 5.2 with its predecessor, Fable 5.1, on safeguard blocks, output speed, and API pricing.

AI agents have had a URL problem for a while. When an agent needs current information but has no search tool, it often makes up the web address it thinks the answer lives at. It might guess something like docs.example.com/v4.2/auth, try to load it, and get a “page not found” error because the page doesn’t exist. Then it either keeps guessing or falls back on outdated information from its training. “Live web search gives AI agents fresh pages.

How much simpler could a data lakehouse get if an ordinary SQL database kept track of its files? That’s the question in this episode of The New Stack podcast. DuckLake is an open-source table lakehouse format created by Amsterdam-based DuckLabs that acts as a traffic cop for data management by storing table metadata inside a standard SQL database, rather than scattering manifest files across object storage.

Anthropic wants developers to build businesses on Claude, but its expansion into enterprise software is raising concerns among founders that the company supplying their models could eventually become their biggest competitor. Gerald Zingraf, president and COO of GlacierGrid, raised the concern on LinkedIn following Anthropic’s Tuesday expansion of Claude’s enterprise lineup, comparing its strategy to Amazon’s private-label business.

Claude Fable 5.2 vs. GPT-6 Astra: A lower list price didn’t mean a lower bill Photo by Igor Omilaev on Unsplash Anthropic is positioning its new model, Claude Fable 5.2, for demanding coding and technical work. Anthropic launched Fable 5.2 on Nov. 9 and reported higher scores than OpenAI’s GPT-6 Astra on two coding benchmarks. Fable 5.2 also has lower standard API rates.

Anthropic has unveiled a self-hosted version of its platform for building and running AI agents. Self-hosted Claude Managed Agents, as it’s aptly called, is available now in private beta. Customers host both the server that coordinates the agents and the sandboxes where the agents run their tools, either in their own cloud account or in an on-premises data center. Credentials and session histories stay on their infrastructure, while requests to Claude go to the inference provider they configure.

AI models are getting smarter, tokens are getting cheaper, and agents are improving. But the context gap sits between the modern enterprise and agentic nirvana, keeping many companies from realizing the ROI they hoped for from their AI investments. As with most problems we’ve seen in the AI era, tech companies are rushing to fill the gap.

DeepSeek’s open-source coding agent is losing points on a major benchmark for a habit developers usually treat as good practice: cleaning up after itself. The problem surfaced when Hugging Face harness optimization researcher Joël Niklaus added DeepSeek Harness to his coding-agent leaderboard, which compares ten harnesses running the same underlying models.

Agentic retrieval is a hot topic for a reason: Every company wants to automate work, but they often just are not ready. It’s also extremely easy — and tempting — to spin up an agent, point it at a mountain of data, and spend heavily on tokens for a lackluster result. To unleash agents that can accomplish work, you’ll need a different approach. You can’t throw a more intelligent AI model at a failing agentic workflow without first sorting out how to provide it with better data. It won’t do much good.