The build vs buy conversation isn't new, but agent sprawl has changed the equation for agent infrastructure. Read on for a practical discussion of the AI run and control layer and when you should build or buy.
Native Teams CTO Dimitris Kountanis worked through a month of automation backlog in late evenings and ended up with roughly twenty governed agents covering most of his engineering lifecycle. Here is what changed the economics.
When technology stops being seen as magical, it starts becoming practical. Now it's AI's turn.
Review the state of agent governance in 2026 and how current infrastructures are evolving to make agents more responsible at scale.
The enterprise non-human-identity-to-human ratio hit 144:1 this year. RBAC was built for the 1.
Visibility isn't governance. A field guide to four "AI agent governance platforms" in 2026 — and how to choose the right one for your ecosystem.
A look at how we use Guild at Guild.
Two independent research groups set out to solve the same problem — governing AI agents at runtime. Both arrived at the same architecture. Here's what they found and what it means for engineering teams.
Production agent reliability is an AI agent infrastructure problem—and the four pillars that solve it live outside the agent itself.
Frameworks build the agent. Models power it. Neither one decides whether your agents survive contact with production.
Production infrastructure was built around determinism. Agents are built around probability. When we collapse reasoning and execution into the same layer, we get outages we can't reproduce and policy violations we can't explain.
Your agent-powered deployment pipeline approved a PR, merged it to main, triggered a rollout, and took down staging.
You wired up three agents on a Thursday afternoon.
The most common failure mode I've seen is brutal in hindsight: months spent making the wrong thing better. Guild.ai CEO James Everingham shares five principles for finding product–market fit faster — from designing to 50% to debugging products into existence.
Most organizations have no idea how many AI agents are running, who owns them, what they can access, or what they cost. This guide covers agent sprawl, the four functions of a control plane, and the implementation framework for managing agents like production software.
In 2008, sharing code meant emailing zip files. GitHub didn't just host repositories — it created network effects that made collaboration the default. AI agents are at that same inflection point. Here's what GitHub for agents actually looks like.
Engineering teams are building impressive AI agents — code reviewers, issue triagers, documentation generators. But sharing them? Someone rolls their chair over and copies the prompt. We've spent fifteen years building infrastructure for code collaboration. For AI agents, we have none of it.
Single AI agents fail in production because trust, control, and isolation don’t scale. Multi-agent systems do.
Your best engineers aren’t slow. They’re distracted. Between CI failures, compliance work, on-call firefighting, and endless context switching, most senior engineers spend less than 30% of their time actually coding. Here’s why developer friction is the real productivity killer—and how specialized AI agents can remove it.
AI can ship code 10x faster, but it can’t feel user pain. Engineering empathy, taste, and the discipline of subtraction are the only true competitive advantages in a world of commoditized software.
