AI that fixes its own mistakes
before touching your cluster.
One platform, three AI engines — Reflexion (SRE), FinOps (cost), and Sovereign (security) — that argue until the fix is safe, then act. Built by people who've been woken up at 3 AM by OOMKilled pods.
🐦 Ruffle: “I make your pager boring. That's the entire job.”
✓ observe p99 latency spike — 2400ms on api-gateway
⟳ actor hypothesis: OOM on inference-worker-7f9b · confidence 0.91
⟳ critic blast radius 1 pod · SLO impact < 5% · approved
✓ act kubectl patch deployment inference-worker --mem=4Gi
✓ resolved p99 → 180ms · MTTR 4m 12s▋
self-correction cycles before the agent acts
The Critic rejects, the Actor revises — over and over — until the fix survives a blast-radius and SLO check. That's the difference between a fix and a confident guess.
Three Engines, One Platform
One platform. Three engines.
Code to infrastructure to cost.
Most teams stitch together a dozen tools for reliability, cost, and compliance. Starling-Ex runs all three as one cognitive flock — in your VPC.
Reflexion
· SREYour SRE never sleeps.
Auto-discovers incidents, investigates root cause, proposes and gates the fix.
MTTR down 70%FinOps
· CostStop cloud waste before it starts.
Real-time cost attribution, predictive scaling, auto-rightsizing — Terraform PRs you can trust.
Cloud waste cut 35%Sovereign
· SecurityCompliance as code.
Policy-as-code and compliance drift detection embedded in the pipeline, not bolted on after.
Audit-ready, alwaysThe Reflexion Loop — Living Flock
One intelligent perch for the whole SRE lifecycle.
Observe like a Shrike. Steer like SteadyHelm. Reflect like Reflexion. Flock like Murmuration. Every incident sharpens the next — until the pager becomes background noise.
Observe
Logs, metrics, traces, alerts, runbooks — ingested continuously across every cluster you run.
Reason
Two brains, not one. The Actor proposes a fix. The Critic argues against it until the plan is safe.
Act — gated
Low-risk fixes execute automatically via GitOps. High-risk ones page a human. You stay in control.
Reflect
Every incident teaches the system. The knowledge base gets sharper. Next time is faster.
Why Warble
The 3 AM page, rewritten.
Without Warble
- ✕Paged at 3 AM. SSH in, grep logs across a dozen services.
- ✕45+ minutes of MTTU — context spread across 8 tools.
- ✕Runbooks that lie. Tribal knowledge that walked out the door.
- ✕AI "ops" tools that demo well and hallucinate in production.
With Warble
- Warble saw the CrashLoop 90 seconds earlier. Hypothesis ready.
- Streaming RCA in under 10 seconds, ranked by confidence.
- Critic verified the fix wouldn't blow blast radius. Gated execution.
- You wake up to a fix waiting in the cockpit — not a fire.
What You Get
Outcomes, not architecture diagrams.
Four capabilities, one cognitive core. Each one maps to a problem you've actually had at 3 AM.
Streaming root-cause analysis
Hypotheses appear in the cockpit as the agent forms them — ranked, confidence-scored. No waiting for a final report.
Gated auto-remediation
The Critic checks blast radius and SLO impact before anything runs. Confident-but-stupid actions never reach your cluster.
AI cost engineering
Token attribution per feature, semantic caching, GPU rightsizing. Treat AI spend like any other SLO.
Every action is a pull request
GitOps-native. Every agent decision has a reasoning trace and a revertable commit. No black boxes.
The Engine
Why two brains beat
one big LLM.
Restart the deployment. Should clear the memory leak.
Rejected — restart drops 1,200 in-flight requests. SLO breach. Propose something reversible.
Bump the memory limit + roll one pod at a time.
Approved. Blast radius 1 pod. Confidence 0.94. Executing.
Proof in Numbers
Engineering metrics, not marketing copy.
Measured with early design partners across SaaS and FinTech.
faster mean-time-to-recovery — hypothesis-driven RCA vs. 14-dashboard context switching
of incidents auto-remediated — humans gated in only on high-risk actions
lower AI workload spend — token attribution + semantic caching, not over-provisioning
No Lock-In
Built on the open-source stack you already trust.
Every layer is a primitive you can swap. Nothing proprietary at the substrate — the intelligence is ours, the foundation is the community's.
Client Results
Real clusters. Real recoveries.
The Rethink
Two products. One clear path to owning your stack.
One-click deployments that live in your clusters. Full GitOps. No SaaS tax. This is how you take back control of infrastructure.
Take ownershipProduction-grade MLOps that actually respects your GitOps workflows and security posture. Reproducible. Governed. Fully yours.
Claim your stackOnce you own the foundation, the real power unlocks: Reflexion agents that watch, debate, and heal — a Living Flock that gets smarter over time, all in your environment.
Join the RethinkQuestions, answered
Frequently asked
The short version of what the flock does, who owns it, and what it costs.
What is Agentic SRE?
Agentic SRE is autonomous site reliability engineering: an AI that investigates an incident, proposes a fix, checks it against your SLOs and policies, then remediates — the way a senior on-call engineer would, but in seconds and without the 3 a.m. page. Warble Cloud's Reflexion engine runs this loop continuously, and every action is policy-gated and reversible.
What is the Reflexion Living Flock?
The Living Flock is Warble Cloud's fleet of cooperating agents that run inside your own cluster: Reflexion (self-correcting Actor/Critic remediation), Starling (the Kubernetes platform), and Warble Brain (model serving and GPU scaling). Together they observe, decide, and heal — birds of a feather working as one operations team.
How does the Reflexion loop work?
Reflexion observes the incident (metrics, logs, cluster state), the Actor proposes a remediation hypothesis, the Critic validates it against SLO impact and policy, and only approved actions execute via GitOps or kubectl. It self-corrects across multiple cycles before acting, and every outcome is recorded as a training signal — so the system improves with each incident.
Is Warble Cloud self-hosted and sovereign?
Yes. Warble Cloud runs entirely in your own VPC or cluster, GitOps-native, with zero vendor lock-in. Your data, runbooks, and incident history never leave your perimeter — you own the stack and can uninstall at any time.
What is Starling MCP?
Starling exposes a Model Context Protocol (MCP) surface so AI assistants and tools can safely query and act on your platform — discovering services, reading metrics, and triggering governed actions through a single, policy-controlled interface.
What does Warble Cloud cost?
Seat-based pricing at $300 per seat. Most teams are production-ready on their own cluster in about five working days, with no credit card to start and cancel-anytime terms.
Make your pager boring.
Seat-based pricing at $300/seat. Production-ready on your own cluster in 5 working days. Cancel anytime.
🐦 Ruffle: “Worst case, you uninstall me and go back to grep. Best case, you sleep through the night.”
The full Living Flock experience (Perch v1) is now in private preview — cockpit.warblecloud.ai