Rootly alternative: the infrastructure context layer

Rootly is a Slack-native and Microsoft-Teams-native incident management platform that bundles AI-assisted root cause hints, automated retrospectives, on-call scheduling, status pages, and predictive incident detection. The Slack-first ergonomics are excellent: declaration, roles, runbook execution, and timeline communication all happen in the chat surface engineers already live in.

Rootly's AI correlates code changes, telemetry, and past incidents to surface likely root cause hypotheses. The change-awareness layer comes from GitHub and CI/CD integrations rather than a dedicated infrastructure change tracker, which means it sees the deploys that flow through Rootly's connected pipelines but not the IAM updates, Helm rollouts, kubectl edits, and managed-database changes that happen out of band. Rootly is incident management first, AI SRE second.

Cloudflare's November 2025 cascading outage is the canonical case for why that distinction matters. Monitoring detected the failures within minutes. Tracing them back to the original internal change took hours, because the dependency chain between the changed component and the affected services was not queryable infrastructure data. CI/CD-only change visibility cannot bridge that gap when the breaking change happened outside the pipeline.

Incident-time questions are causal and temporal, not statistical. "Which deploy caused this?" and "what's different between the system at 14:00 and the system at 15:00?" require an indexed model of the production stack, with every IAM change, every Helm rollout, every Terraform apply, and every commit recorded as a versioned node. CI/CD events alone miss the out-of-band changes that quietly produce the next outage.

That model is what Anyshift builds. The platform connects to AWS, GCP, Azure, Kubernetes, and your git provider in roughly 30 minutes, with no in-cluster agents and no instrumentation work. Every infrastructure mutation is recorded as a versioned node regardless of whether it flowed through CI/CD or someone's terminal, which is the same architectural argument we walk through here.

The methodology behind Annie, Anyshift's investigation agent, is documented in Agentic Context Engineering, a paper authored with researchers at Stanford and SambaNova Systems and accepted at ICLR 2026. The same technique has been live in production since October 2025, where it has cut root-cause-analysis time by 30% on real customer incidents.

Anyshift is not a replacement for Rootly; it adds an investigation engine behind Rootly's process layer. Rootly declares the incident in Slack and runs the response timeline. Anyshift runs the investigation against the versioned graph in parallel and posts a root-cause report into the same Rootly channel, which Rootly then surfaces in the auto-generated retrospective.

The net effect is a shorter time to "we know what changed", retrospectives that read like investigations rather than narrations, and infrastructure change visibility extended beyond the CI/CD pipeline. Anyshift customers running this pattern alongside Rootly report MTTR reductions of 85% or more on incidents where the change happened outside the deployment surface Rootly already watches.