How Kernel keeps shipping fast with Firetiger
Mason and Sayan, founding engineers at Kernel, talk about what changed when Kernel turned on Firetiger Change Monitors across their pull request workflow.
Rustam Lalkaka
Agentically optimizing LLM prompt cache TTLs for fun and profit
A case study on production objective hill climbing
Firetiger runs a few hundred large language model (LLM) agents in production, and prompt caching is a critical tool to manage the cost of running such a workload. Properly setting cache time-to-live (TTL), how long a cached prefix survives before
Firetiger Change Monitors: does your PR do what it says on the tin?
Today, we're making Change Monitors generally available for all Firetiger customers.
Firetiger Change Monitors pair with every pull request to form a per-change safety net: an agent reads your diff, studies the systems it touches and how they behave in prod, and drafts a targeted post-deploy
Agent SLOs: Grounding autonomous agents in metrics that matter
Today we're shipping Agent SLOs, a new feature that gives every Firetiger agent a structured way to track the health of the missions it's assigned. Agents now define their own service level objectives (SLOs), evaluate them every session, and use those evaluations to triage the issues
Introducing Firetiger
Observability is dead. Long live outcome engineering.
Today, we’re excited to announce Firetiger. You and your AI agents write code. Firetiger makes sure it works.
Our team, Achille, and I have plenty of incident war stories to share from our time helping build Cloudflare, Segment, Twitch, and more. The
How do I get started with Firetiger?
Step 1: Connect your GitHub repository and send us observability data
Send as much detail as you’d like, with billions of events of any dimensionality or cardinality a month included with all plans. We have out of the box support for ingesting data from a multitude of sources, including