Issue #11
MCP servers are becoming production dependencies for agent systems. How to inventory ownership, permissions, observability, and failure modes before they become hidden infrastructure risk.
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AI teams accumulate models faster than they build controls. How to manage model sprawl with registries, drift monitoring, rollbacks, and consolidation.
Issue #9
AI coding tools have genuinely made teams faster. The Harness 2026 State of DevOps report confirms it: AI coding adoption is up, velocity metrics are up, output is up. The same report notes that security and DevOps maturity haven't kept pace with the acceleration. More code is shipping,
Issue #8
A single agent handling predictable traffic is the easy case. Add a gateway, configure it correctly per Parts 1 and 2, and it works. The failure modes at scale are different in kind. An indirect prompt injection embedded in a document your agent was summarizing. A multi-agent workflow where a
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The gap between 'I added a gateway' and 'my gateway is actually working.' Four configuration decisions that separate coverage from false confidence.
Agent Infrastructure
An agent gateway is the infrastructure layer between your agent and everything it talks to. This is Part 1 of a three-part series on the control plane your agent is missing.
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There's a category of AI bug that doesn't announce itself. The agent worked fine last week. Nothing in your codebase changed. No deployment, no model update, no infrastructure incident. But the output quality is worse, customer support tickets are up, and when you dig in, the
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The most common mistake developers make when choosing an agent framework is treating it like a software comparison article. They pull up a table of features, check off which framework supports memory, which has the cleanest API, which has the biggest community. Then they pick one and spend three weeks
Issue #3
The tutorials all end the same way. The agent works. It classifies the ticket, routes the email, summarizes the document. You run it a few times, it looks good, you ship it. Three weeks later it's hallucinating customer data at 2 AM, retrying in an infinite loop, burning
Issue #2
When an agent fails, most developers look for a better model. The problem is almost always the context.
Issue #1
The productivity gains are real. They're also narrower, weirder, and more conditional than the marketing wants you to believe.