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Since releasing our hosted MCP server last year, we've been thrilled to see customers not just adopt it but build Honeycomb deeply into their agentic development and observability workflows. Users have embraced it, leveraging Honeycomb to stay in conversation with their code and understand how it runs in production.

While AI have usage has been growing steadily for the last several years, the LLM models noticeably improved around the end of 2025. Specifically, they become more viable for software development. We are seeing the results. The feature and product delivery has picked up. One way to visualize this is by looking at the number of pull requests for your organization / software development teams. This chart shows the number of Github pull requests created by a team. Can you spot when AI usage increased?

The pace of AI-assisted development has outgrown how most teams actually ship. Harness is closing that gap. Engineering teams are generating more shippable code than ever before — and today, Harness is shipping five new capabilities designed to help teams release confidently. AI coding assistants lowered the barrier to writing software, and the volume of changes moving through delivery pipelines has grown accordingly. But the release process itself hasn't kept pace.

KubeCon + CloudNativeCon Europe 2026 recently brought the cloud native community to Amsterdam. We were there all week bouncing between the booth, a Braintrust event with engineering leaders from across the community, and more hallway conversations than we can count. One talking point dominated the week: AI is shipping code faster than most engineering orgs can govern it. It also became clear that we weren't the only ones talking about this challenge.

APIs sit at the core of modern digital infrastructure. From e-commerce checkouts and payment processing to SaaS platforms and mobile applications, APIs move the data that keeps systems running. But APIs do not operate as a single unit. They are made up of individual endpoints, and each endpoint represents a specific function or resource that users depend on. As organizations shift toward microservices, cloud native applications, and third party integrations, the number of endpoints increases rapidly.

APIs power modern applications. Every login request, product search, payment authorization, and mobile app refresh depends on an API responding quickly and reliably. When latency increases, users feel it immediately. Pages stall. Transactions hang. Confidence drops. Most engineering teams measure API latency. Fewer truly monitor it. There is a difference. Many teams track average latency in dashboards and assume performance is healthy.