Cognition ( @cognition) now triggers more Devins asynchronously than interactively. As @ido_pesok puts it, async agents are only useful if developers can trust what they come back with. In a cloud-native system, that trust is a runtime problem. New on @thenewstack, Signadot CEO @arjuniyer_ on why a green test run against mocks says nothing about cross-service behavior, why where the verification loop closes decides what a defect costs, and how to close that loop before the PR. thenewstack.io/verifying-asy…

We gave two @AnthropicAI Claude Code sessions the same task. Same model, same prompt to make a change to a microservice. The results were very different. 🧵

New tutorial: closed-loop microservices validation with @AnthropicAI Claude Code and Signadot. The agent proves a change against your real system before the PR, not after. signadot.com/docs/tutorials/…

New in Signadot: Sandbox Analyze. An AI feature that makes managing your environments even more frictionless.

Talked to a VP of Eng at a series-B last week about their coding agent rollout.  Code volume up 4x. Merge rate flat.

Shipped @Signadot Plans today. Really excited about this one. We've been focused on giving teams building microservices the ability to ship faster with confidence using coding agents. That means closing the loop between writing and verifying, end to end, before a human steps in. Lightweight, production-like environments were one half of that. Plans are the other half. Small, reusable validation workflows that agents author from natural language and run inside your Kubernetes cluster, governed by the platform team. This release turns Signadot into the most comprehensive, agent-native platform for building and validating microservices on Kubernetes. signadot.com/blog/signadot-p…

Signadot Plans is now in beta. Reusable validation workflows your coding agents author from natural language and run against a real Kubernetes environment, ready in seconds for any change.

Addy Osmani's recent post on agent harness engineering nails it: the quality of an agent’s feedback loop is more important than the model.  For agents building simple apps, that feedback is straightforward. For ones building in cloud-native systems, it isn't.

Very excited to announce signadot-validate. A new skill that lets coding agents close the loop in cloud-native systems. 🧵 signadot.com/blog/introducin…

@bcherny (creator of Claude Code) says the best way to 2-3x what you get out of Claude is to give it a way to verify its work.  But for cloud-native apps, a "local check" isn't enough. Your agent needs the full system context. In this article, we explore why closing this loop is the next big leap for engineering teams: signadot.com/blog/claude-clo…

Coding agents have converged on the same pattern: agents that verify their own work before handing it off. Easy against a single codebase. Breaks against cloud-native systems, where bugs live at the seams between services. Our CEO @arjuniyer_ wrote a piece for @thenewstack on what it takes to close the loop: thenewstack.io/claude-cloud-…

The productivity of AI coding agents is often a hidden tax on the developer. If an agent can’t run its own changes in a production-like environment, the correctness work falls back on the human.

Agents produce code faster than teams can validate it in distributed systems. That's the new release-velocity ceiling. Signadot gives agents and CI pipelines isolated slices of a live Kubernetes cluster, on demand. Real dependencies, not mocks. Feedback in seconds. Video explainer below 👇

Stop babysitting your AI agents. 🤖 Our new tutorial shows how to build a closed-loop workflow with @OpenAI Codex. 🧵

Welcome aboard, Laurel.ai! As the engineering team at @getlaurel adopted coding agents to accelerate their output, they needed a validation layer that could keep pace with their AI-first velocity. We're excited to have Laurel join the growing list of companies using Signadot to ship cloud-native software faster and with more confidence. Thrilled to support their journey!

Introducing Signadot Skills. Signadot Sandboxes give coding agents runtime context against your live cluster. 🧵

You can now train @physical_int style robots in 1 day for only $5k. Anvil’s devkits have all the hardware, software, controls, cameras, and more ready-to-go. (1/5)

Hot take: AI agents don't do software engineering. They write code. Completely different discipline. Writing code = pattern recognition. LLMs are great at this. Software engineering = trade-offs, constraints, cross-domain context no model has. Rote and mechanical? Let the agent do it. Strategic and novel? That's the developer's job. The developers who win won't write the most code. They'll make the best architectural decisions — then let agents execute. #SoftwareEngineering #AIagents #Coding #TechTrends #SoftwareDevelopment #GenerativeAI

I’m seeing a recurring theme in my conversations with platform teams: agent-generated PRs are moving faster than validation processes can handle. Things are breaking. The good news? If you’re already using @IstioMesh, you’re 50% of the way to a fix. Here’s why: 👇

When agent-generated PRs move faster than your validation process, things break. Fortunately, @IstioMesh users are already 50% of the way to a fix. Let’s look at how: 🧵