Building Reliable AI Agents at Scale

What we've learned from running millions of agent actions across codebases, APIs, and production workflows.

Building an AI agent is easy.

Building an AI agent that consistently performs useful work across thousands of environments, repositories, and workflows is a completely different challenge.

Over the last year, we've learned that reliability—not intelligence—is often the biggest obstacle to deploying AI systems in production. Models continue to improve rapidly, but production systems require much more than model quality alone.

They require infrastructure.

The challenge of real-world execution

Most AI demos happen in controlled environments. Real organizations are far messier.

Repositories contain years of accumulated complexity. APIs fail. Permissions change. Documentation becomes outdated. Systems evolve faster than instructions can keep up.

An agent operating in production must be able to navigate uncertainty, recover from failures, and adapt to changing conditions without constant supervision.

This requires much more than a strong model.

It requires robust execution systems, observability, retries, permissions, validation, and safeguards at every layer.

Reliability is a product feature

One of the biggest lessons we've learned is that users care far more about consistency than occasional brilliance.

A system that succeeds 95% of the time creates trust. A system that succeeds spectacularly one day and fails unpredictably the next creates hesitation.

As a result, much of our engineering effort goes into making agent behavior predictable and observable.

Every run is tracked. Every tool invocation is recorded. Every decision can be inspected. When something goes wrong, teams can understand why and improve the workflow.

Reliability isn't a byproduct of the platform—it's one of the core features.

Designing for humans

Despite advances in autonomy, we don't believe humans disappear from the workflow.

The best systems combine autonomous execution with human oversight. Agents should be capable of acting independently while still providing visibility and control when needed.

This philosophy influences everything we build, from run traces and permissions to approvals and deployment controls.

Autonomy should increase confidence, not reduce it.

The future of engineering systems

We're entering a world where software doesn't just assist developers—it actively contributes alongside them.

Agents will write code, review changes, update documentation, manage infrastructure, and coordinate workflows across organizations. As these systems become more capable, the infrastructure supporting them becomes increasingly important.

Building reliable AI agents isn't just a model problem. It's an engineering problem.

And solving that problem is one of the most important challenges in software today.

We're excited to continue building the infrastructure that makes autonomous software possible.