Explore everything we've researched, built, tested, and learned.

How Should an AI Agent Recover From Failure?

AI agents will fail. The real engineering problem is what happens next. This technical dive explores how AI agent infrastructure can detect failures, diagnose what went wrong, retry intelligently, preserve state, roll back unsafe changes, verify recovery, and escalate to a human when automation should stop.

What Happens When an AI Agent Gets Stuck?

An AI agent failing isn't the interesting part. The interesting part is what happens next: can it recognize the failure, diagnose what went wrong, recover without making things worse, and know when it should stop?

How Much Can an AI Agent Build Without Human Intervention?

This research examines AI agent automation through real coding-agent benchmarks, controlled environments, autonomous execution loops, recovery, testing, and the infrastructure required for greater AI agent autonomy.

Can AI Agents Build and Run Applications? A Practical Experiment

Can AI agents actually build and run real applications? We examine what happens when an AI coding agent gets access to a real execution environment through MCP, using Causly Server as a working example.

What Changes When AI Gets Its Own Environment?

What Infrastructure Does an AI Agent Actually Need?

AI agents are getting better at using tools. But real work needs more than tools — it needs a place to execute, persist, experiment, and operate. So what should an AI agent actually have?

Tools vs Environments: What AI Agents Actually Need

Tools give AI agents capabilities. Environments give those capabilities somewhere to actually work. Here's why that difference matters.

Why AI Agents Need Their Own Environment

AI agents can have access to dozens of tools. But where do they actually work?

Why AI Agents Need More Than Tools

AI agents are getting better at using tools. But if we want them to actually do the work, tools alone aren't enough.