Reflexion Beta:
Stress-Test Our Agentic Engine
Enterprise GenAI fails when agents operate blindly. We built a self-correcting, multi-agent orchestration engine. We need hardcore engineers to try and break it.
What You Will Be Testing
Five adversarial surfaces. Real production infrastructure. No sandboxes.
The Reflexion Loop
Our agents don't just generate; they evaluate. Test our multi-turn self-correction routing to see how efficiently the system catches its own logic flaws and hallucinations before triggering external tools.
The Dual-Brain Architecture
We decoupled the Action Brain from the Knowledge Brain. Test how efficiently it manages complex RAG pipelines under load.
Air-Gapped Context Perimeters
The entire multi-agent system is locked down using isolated network service perimeters and least-privilege identity access. Try to find the weak points in our exfiltration defenses.
Token & Context Management
Test our semantic parsing and context-caching pipelines against massive unstructured datasets to validate how we prevent token bloat.
Serverless GPU Scaling
Our compute layer utilizes dynamic mathematical modeling for concurrency on serverless GPUs. Stress-test our auto-scaling logic and see if you can trigger unacceptable cold-start latencies.
The Core Brain Architecture
The Action Brain manages workflow execution and external tool calling. The Knowledge Brain powers high-speed vector retrieval and semantic context. Both operate within air-gapped network perimeters on serverless GPU infrastructure, governed by continuous Reflexion loops for self-correction.
No Marketing Fluff
🦅 ShrikeOps — Kubernetes Manifest Scanner
Production-grade static analysis for Kubernetes manifests. ShrikeOps runs Polaris, kube-score, Pluto, and OPA policies in a single pass — returning a scored report with severity-ranked findings and remediation guidance. Plug it into your GitHub PRs via our webhook and catch misconfigs before they reach production.
Walk through one incident.
Five stages from page to fix. Every Reflexion run takes the same shape — only the depth of each stage changes.
- 01 · Observe
An alert lands on the Reflexion Engine.
Starling streams the live cluster signal — pod status, recent deploys, error budgets, dependent services — into the Engine's working memory. Brain attaches the last 90 days of related incidents from the long-term memory.
- 02 · Hypothesise
Actor agent proposes a fix.
The Actor reasons over the observation packet and the historical context, then drafts a concrete remediation: a config rollback, a horizontal scale-out, a NetworkPolicy patch — whatever the runbook implies. It writes the plan as a typed proposal, not free-text.
- 03 · Critique
Critic agent attacks the proposal.
The Critic challenges the plan from a different vantage: blast-radius, dependency reachability, recent change windows, on-call awareness. Anything below the confidence threshold gets sent back. Anything above gets marked human-gated or auto-actionable based on policy.
- 04 · Act
Action Brain executes — under guardrails.
Auto-actionable changes execute via the existing GitOps surface (PR + auto-merge for known-safe patterns) or directly through Starling's typed RPCs. Human-gated changes page on-call with a one-click apply. Either way the change is observable end-to-end.
- 05 · Reflect
The loop closes — and trains the next one.
After the change, the Engine watches the SLO it was trying to fix. If the metric returns, the incident is annotated and stored in Brain so the next Critic has one more example. If it doesn't, the loop re-enters at step 02 with the new ground truth.
Questions teams ask before pilot.
The same things every platform lead wants to know — answered without the marketing layer.
- Yes, but only inside a policy you write. Each change type carries a confidence threshold and a blast-radius bound. Anything that exceeds either pages on-call with a one-click apply instead of executing. Out of the box every destructive action is human-gated; teams progressively widen the auto-apply set as confidence builds.
Question not here? Ask us directly.
Request Beta Access
If you spend your days optimizing infrastructure, managing Kubernetes clusters, or building MLOps pipelines — we want you on this list.
Already using Warble Cloud? Explore Agentic Ops