Operations | Monitoring | ITSM | DevOps | Cloud

TL;DR - We made a way to use Cycle with AI models for doing things like deploying applications faster and diagnosing complex issues. If you'd like to get started right away with Cycle's Hosted MCP, check out our documentation. Today, we're launching a brand new way to interface with the Cycle Platform, that just a year or two ago would have felt entirely like science fiction. Our first ever AI-oriented tooling, Cycle's Hosted MCP, is live today.

Running one AI coding agent is productive. Running five of them at once across three repositories? That’s a coordination problem. Agentic development environments (ADEs) give you a single surface to orchestrate multiple agents, review what each one changed, and get code merged. Kepler by GitKraken is one of the most capable options in this growing category. It’s built around agent-agnostic orchestration and Git workflow control from backlog to merged PR.

At Tigera, we spend a lot of time thinking about agent security: identity, policy, runtime controls, and the record left behind after an agent acts. Coding agents create an interesting problem because, in most organizations, they didn’t arrive through the front door. Few companies ran a platform evaluation and rolled Claude Code out to 500 developers. Developers installed it themselves.

This isn't a teardown. This stack is a genuinely reasonable way to start. An AI-first editor handles day-to-day writing. A terminal-based coding agent takes on tasks that need more autonomy: a full feature, a migration, a stubborn bug. A few scripts connect the pieces, trigger a run, and post a result somewhere. An observability tool checks what happened after the fact. Every part of that is a real, capable tool. For the first few months, on a small team, it works.

Over the last several years, organizations have invested heavily in visibility, automation and artificial intelligence. Yet many still face a common challenge: They have more dashboards than ever, but getting a reliable answer often requires manual investigation, reconciliation and validation before action can occur. The problem is rarely a lack of data. More often, information lacks the shared context people need to understand it and act.