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The New Stack | DevOps, Open Source, and Cloud Native News

Agentic development hinges on verification. For cloud-native software, that is a runtime problem. AI agents need infrastructure: Why Europe’s regional cloud strategy matters Transform your AI coding agent into a deterministic Java Spring expert WeAreDevelopers is coming to the US to give unsung developers a bigger voice Cleaner AI training data, fewer bugs: Sonar’s SonarSweep explained Observability overload is drowning engineers Google’s DiffusionGemma is 4x faster than its other Gemma models Fable 5: Guardrails and burn rate are annoying users, who say it’s still better than Opus 4.8 The Anthropic leader who built Claude Code says he ditched prompting — now he just writes loops. AWS can now mathematically prove your VMs are isolated Microsoft pulled 73 GitHub repos after malware attack — but still won’t say who’s compromised Databricks wants to kill the “email me a file” problem for AI agent skills Ramp bets forward deployed engineers can do what off-the-shelf finance AI can’t Git real: AI agents aren’t just for solo developers anymore Anthropic launches Claude Mythos/Fable 5, but you better try it soon Spring is 23 years old. AI just made it a security emergency. This AI agent startup ditched Anthropic for DeepSeek — and says it’s saving millions When your data model is the bottleneck: lessons from Medium’s feature store How long before we stop reading the code? The tokenmaxxing party is over, and Revenium is mopping up How AI is solving the memory crunch it created Microsoft’s pitch to enterprises: Ditch Azure Repos for GitHub, despite its rocky reliability record Claude Code’s biggest upgrade yet ran 5 agents at once — here’s what happened Why Anthropic just doubled Claude Cowork limits at no charge For years, Apache Cassandra handed this work to your team — 6.0 takes it back “A dangerous combination”: The 2 factors that can “corrupt” AI agent workflows With Foundry, Microsoft bets the enterprise AI battle is about reliability, not capability Microsoft unlocks Visual Studio for developers left behind by its own AI AI teams now deploy 1,000 times a month. Your pipeline wasn’t built for that. Microsoft just made the agent runtime free — and kept everything around it “Whoever builds the most joyous product wins”: The agent war begins Netlify CTO Dana Lawson: Writing code is no longer the job From Jupyter Notebook to production: How to ship AI systems that actually work OpenClaw used Gavriel Cohen’s code and exposed the AI Agent accountability problem Replit shows how vibe coding is getting its own financial stack — and a path to profit Cloudflare aqui-hires VoidZero: Did a piece of the open web just stabilize, or become more brittle? Cursor cuts prices and adds enterprise spend controls amid “tokenomics” reckoning Google Gemma 4 12B nearly matches 26B benchmarks — and runs on your laptop Snowflake thinks it knows what’s really slowing developers down Autonomous agents have met their biggest challenge yet: The database. Why agentic AI makes the ops platform the most important layer in the enterprise How to dramatically improve enterprise security alert tuning to battle cyberattacks Why the need for humans won’t disappear in the age of autonomous databases How to secure Kubernetes in the age of AI workloads Asana says its new AI “chief of staff” turns your Slack chaos into trackable work Nvidia’s best model is now live Mate Security’s Asaf Wiener made every backend engineer a model router. He’s right to. 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Here’s why. Why GPT-5.4, Claude, and Gemini can’t agree on basic, real-world facts Replit’s vibe coding platform just got a Visa-backed identity layer for AI agents — and it changes how agents spend money Opus 4.8 Made Claude Smarter. Token Discipline Got Urgent. Why Linux creator Linus Torvalds gets angry hearing “99% of code is AI” Vendor neutrality isn’t magic: A hard look at the OpenTelemetry ecosystem “The AI did it” won’t save you when EU regulators come knocking The fix for soaring AI cloud bills exists — so why won’t we trust it? AI is shipping code faster than security was built to handle Why AWS scrapped OpenSearch’s architecture to chase agent workloads Claude Opus 4.8 is here: effort controls, dynamic workflows, cheaper fast mode, better honesty, less deception Percona celebrates 20th birthday with new foundation — and a goat cake Why OpenAI and Anthropic are hiring forward deployed engineer teams Claw-style AI agents are coming to the enterprise. The governance infrastructure is still catching up. 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With Google’s debut, the most important AI agent feature is now the most boring one
Janakiram MSV · 2026-05-27 · via The New Stack | DevOps, Open Source, and Cloud Native News

When Google announced last week at its I/O conference that it was repositioning Antigravity as a platform for developing and managing teams of autonomous AI agents, the pitch quickly took on a familiar arc.

For anyone paying attention to the space for the previous two months, the following felt a bit like déjà vu: One API call to the Antigravity agent spins up a remote Linux sandbox where the agent reasons, calls tools, runs code, and browses the web. You extend it by writing an AGENTS.md file and a SKILL.md file, register it as a named agent, and write no orchestration code.

I have watched this product ship twice already in the last two months, from two other vendors, and this trend says everything about how important managed agent runtime has become — it has become so important that it has become unimportant, a non-factor, because many labs are adding the service.

The same runtime shipped three times in six weeks

Anthropic shipped Claude Managed Agents into public beta on April 8. The pitch was that infrastructure — not intelligence — had become the bottleneck for production agents, so Anthropic would handle the agent loop, the sandbox, the state, and the credential scoping.

AWS followed on April 22 with a preview of a managed harness within Bedrock AgentCore. The runtime itself predates that release, having shipped in 2025, but the April update added the piece that matters here, a configuration-first harness that declares the model, tools, and instructions and runs the loop without bespoke orchestration code.

Then, Google at I/O, with Managed Agents in the Gemini API, did the same thing again.

Three vendors landed nearly the same runtime shape inside six weeks. Each launch post tells the same story: Building a production agent used to mean stitching together a model API, a sandbox, an orchestration layer, and hosting, and that the managed version collapses all of that into configuration and a handful of API calls.

When three companies independently converge on the same product within six weeks, the runtime has become table stakes rather than a reason to pick one platform over another.

The Markdown file is becoming the config standard nobody voted on

Google’s Managed Agents are defined by AGENTS.md and SKILL.md. Anthropic shipped Agent Skills as Markdown directories last year, and SKILL.md is now load-bearing across Claude Code and Managed Agents. AGENTS.md is an open format that grew out of work across OpenAI Codex, Cursor, Amp, Jules, and Factory, now sits in more than 60,000 open-source repositories, and is stewarded by the Linux Foundation. AWS leaned the same way, shipping prebuilt skills for Claude Code, Codex, Cursor, and Kiro alongside its harness.

So the agent is defined in a plain-text file that a developer can read, diff, and check into Git, with no proprietary DSL and no visual builder holding the definition hostage. The same file describes a Claude agent, a Gemini agent, or an AgentCore agent with very little edited between them. Models will keep leapfrogging each other on benchmarks, but the Markdown config is quietly becoming the portable layer beneath them all, the way a Dockerfile became the unit of a container long before anyone agreed it should.

What it means for the developer choosing now

For a developer picking an agent platform today, whether a lab has a managed agent runtime is no longer the deciding factor because Google, Anthropic, and AWS all offer it. The decision moves to the boring questions: where your data sits, what a session-hour costs, which model runs underneath, and how hard it is to leave when the next model is better elsewhere.

The honest counterargument is that Markdown portability is shallow today. An AGENTS.md written for Gemini still assumes Gemini tool semantics, and moving it to Claude is not a non-starter. If the labs deliberately fork the format to make migration painful, the standard fractures before it sets. But the incentive runs the other way, because the vendor that makes its agents easiest to define also makes them easiest to leave, and right now, every one of them wants the developers more than it wants the lock-in.

The config file is where the next standards fight gets decided, so that is the thing to watch.

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