The Latest in AI Development

If you're building a product on top of Runloop devboxes, your end users probably shouldn't see Runloop tunnel URLs. Today we're shipping a small but useful feature: a new X-Runloop-Host header that lets you front Runloop tunnels with your own domain without rewriting the request.

Runloop Launches Remote Agents SDK: An SDK for Working with Remotely Hosted Agents

Introducing Axons: Runloop’s secure, distributed event stream for scalable, multi-client agents with audit trails, structured state, and full suspend/resume.

The OpenAI Agents SDK fixes an important part of this by giving developers a clean way to define agent behavior. You can structure reasoning, attach tools, and build workflows that actually resemble programs.

Now you can run an agent, suspend the box, and wake it up again when you need it

This feature addresses a fundamental challenge: AI agents need controlled access to external resources. Network Policies make network access explicit, auditable, and enforceable—so teams can safely run agents, limit outbound traffic, and meet security and compliance requirements by default.

A practical walkthrough of building production-ready AI agents for tax form document processing - with agentic architectural patterns you can apply to any compliance-sensitive use case.

rli released! rli is a TUI for developers who want the ability to manage devboxes without leaving their terminal, scriptable commands for automation, and faster workflows for repetitive tasks

Discover the general availability of our Object Storage SDK: a first-class way to ship binaries, datasets, and runtime resources onto devboxes.

RAFT mixes RAG and fine-tuning to boost LLM performance in specific fields. It improves accuracy and makes models faster and more useful for real tasks.

Q-learning helps LLMs make choices and handle tasks. It improves reasoning and make AI more useful in jobs like coding agents.

How reinforcement learning moved from research labs to powering modern LLMs and Runloop.ai’s self-improving agent workflows.

Learn LLM fine-tuning methods like PEFT, LoRA, RLHF, and DPO, with practical tips to improve models after pre-training for real use.

Knowledge distillation makes big AI models smaller and faster without losing much quality. It helps run powerful tech on phones and other small devices.

Trajectory, the continual learning platform, consistently runs 10,000+ burst concurrent devboxes on Runloop for training and fine-tuning models. Their workload looked nothing like a demo: many concurrent sessions, long-poll control loops, older Ubuntu blueprints with pinned dependency graphs on top.

Jer Crane's AI agent deleted his production database in nine seconds. The instinct when something like this happens is to write better rules. That's the wrong fix

Discover the new Runloop Agent API. Learn how its pluggable, versioned design helps streamline your operations. Explore use cases & examples!

Stripe Projects allows you to provision a full Runloop account with devboxes ready for agents, full access to pro plan features, and real credentials in your environment. No dashboard and no copy-paste keys.

Protect Your API Keys and Reduce Context Window Bloat with MCP Hub

How to Meet Your Compliance & Regulatory Needs when Running AI Agents with Runloop

Runloop announces AI Gateway: a proxy to guard your secrets

Tunnels are now faster, simpler and more secure.

Runloop is the safe and comfortable cage for OpenClaw 🦞

Discover the hidden flaws in SWE-bench, a widely used benchmark for AI coding agents. Learn why deeper evaluation matters for real-world performance.

AI-generated code needs more than benchmarks. Real tests and evaluation help check quality, security, and performance in real use.

Learn how to verify AI-generated code with real tests, logs, and observability. Go beyond pass/fail to ensure reliability in production systems.

Struggling to assess AI-generated code quality? Learn how to evaluate correctness, efficiency, and security for comprehensive results.

See the evolution of AI code benchmarks from simple tests to SWE-bench and LiveCodeBench, measuring integration, system design, and engineering.

How AI agents can fix their own code over time. A look at the tools, the wins, and what still holds them back.

Function-calling and MCP help shape LLM output for real use. They make AI more reliable, but each has different strengths and use cases.

MCP lets LLMs plug into real tools and data. With support from GitHub, Slack, Cloudflare, and Sentry, it makes AI way more useful in real work.

Function calling lets LLMs do real actions, not just text. It can order stuff or automate tasks using JSON schemas and tools like LangChain.