We believe AI agents should learn from experience.

Today, agents generate an endless stream of failures, feedback, traces, and successful outcomes. Yet most of that experience is discarded. Improvements are applied manually, regressions reappear, and learning remains fragmented across prompts, memory systems, benchmarks, and coding agents.

We believe a new layer is missing.

A learning infrastructure that transforms experience into durable improvement.

One that turns failures into replayable learning environments. One that identifies where the agent broke across prompts, tools, memory, workflows, and models. One that continuously optimizes the entire agent stack while preserving what already works.

That is the problem RELAI exists to solve.

We are building the continual learning engine for AI agents: a system that converts experience into continuous, regression-aware improvement.

This mission sits at the intersection of frontier AI research and decades of experience building reliable systems. It requires breakthroughs in continual learning, optimization, evaluation, and agent architectures. It requires exceptional engineering. And it requires a team obsessed with advancing the state of the art.

We are researchers, engineers, entrepreneurs, and builders united by a common belief:

The next generation of AI agents will not be programmed. They will be trained by experience.

That's the future we're building at RELAI.