Where Anyshift gets covered: industry roundups, research collaborations, partner deep-dives, and interviews with the team building Annie, the AI SRE agent on top of a versioned infrastructure graph.
Recent mentions, interviews, and collaborations
Curated, dated, and linked. Sorted most recent first. Spans engineering deep-dives, industry roundups, peer-reviewed academic research, and interviews with the founding team.
Anyshift is the architectural outlier in this guide. It reasons from a versioned graph of the infrastructure itself, not from telemetry.
Annie cuts root-cause time by 30% in production, measured in the Stanford / Berkeley / SambaNova ICLR 2026 paper on Agentic Context Engineering.
GraphRAG enables root cause analysis grounded in actual infrastructure topology, not telemetry correlation.
A 30% reduction in mean time to root-cause, measured from Anyshift's internal production evaluations of the ACE method inside Annie.
AI makes it feel like everything is possible, so the real risk is going too far.
If the future of AI SRE is context-driven, then Anyshift is certainly building the 'map' to get there.
ACE matches the top-ranked production-level agent on the overall average and surpasses it on the harder test-challenge split, despite using a smaller open-source model.
This client had over 15 AWS accounts, 25,000 instances, and 200 GitHub repositories, resulting in millions of nodes and edges in Anyshift's knowledge graph.
Your infrastructure is a graph. You need to have this context, this deterministic context, about your own infra.
For interviews, briefings, product demos, or a copy of the ACE-at-Anyshift case study, the fastest path is a short note to the team.
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