AI that fixes its own mistakes
before touching your cluster.
One brain watches your cluster. One brain thinks. They argue until the fix is safe — then they 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.
The Reflexion Loop
One platform for the whole incident lifecycle.
Most tools watch, or alert, or remediate. Warble closes the loop — observe, reason, act, and learn — so every incident makes the next one shorter.
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.
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.”