Reflexion Engine deploys Actor/Critic AI agents on Vertex AI that observe, reason, and remediate Kubernetes incidents before your on-call engineer finishes their coffee. Probabilistic reasoning, not brittle runbooks.
Not a consultancy. An engineering team that ships production-grade agentic infrastructure.
Reflexion Engine replaces deterministic runbooks with probabilistic adaptation. Actor/Critic agents on Vertex AI handle novel failures traditional automation cannot anticipate.
ShrikeOps MCP Bridge lets AI agents reason over live cluster state. Every manifest scanned by Pluto, Polaris, kube-score, and OSV.dev before it reaches your estate.
No PII-laden telemetry leaves your perimeter. Vertex AI, AlloyDB, and Cloud Run locked behind VPC Service Controls. Pass FinReg audits in 48 hours — JPMorgan/BNY-grade compliance by design.
Mathematical rightsizing: VM changes only execute if projected SLO compliance stays ≥95%. Cut token hemorrhage 40–60% via intelligent context caching.
Before & After · Reflexion Engine
Mean Time To Recovery
Hypothesis-driven RCA vs. 14-dashboard switchingTokens Per Incident
Sub-1K targeted actions vs. monolithic LLM callsBaseline Cost / Month
Mathematical rightsizing, not over-provisioningWe built the Reflexion Engine because we were tired of 3 AM pager duty for incidents that follow the same 10 patterns every single time.
Observation Brain ingests telemetry. Reasoning Brain hits AlloyDB pgvector in <100ms. Action Brain executes Terraform/kubectl. Context bloat eliminated.
Cloud Run with pre-warmed instances. First byte <80ms. No container spin-up during a production incident.
Vertex AI, AlloyDB, Cloud Run inside VPC Service Controls. Incident telemetry never leaves your GCP org. FinReg compliant by architecture.
Every remediation action is gate-checked against SLO projections. Drops below 95%? Action blocked and escalated. Automation with a kill switch, always.
Architecture · Dual-Brain Reflexion Engine
Knowledge Hub
Field-tested patterns from production agentic systems. No theory — only what works at scale.
Actor/Critic, ReAct loops, and multi-agent topologies. When to use each pattern, and how to avoid the coordination traps that stall most teams.
ArchitectureSLO-guarded execution, blast radius controls, and human-in-the-loop escalation. How to let agents act autonomously without losing sleep.
SafetyToken cost attribution, latency percentiles, and context window budgeting. Instrument your agentic pipelines before the invoice surprises you.
ObservabilityEmbedding strategies, pgvector indexing, and retrieval latency targets. Build RAG that returns relevant context in <100ms, not 2 seconds.
DataVPC-SC perimeters, zero-exfiltration architectures, and FinReg compliance. Deploy AI that auditors actually approve.
SecurityModel Context Protocol bridges, tool schemas, and agent-to-cluster communication. Give your agents real infrastructure access, safely.
IntegrationMarketplace
Engagement-driven consulting around our products. We ship outcomes, not slide decks.
A structured assessment of your infrastructure's readiness for agentic automation. We map your incident patterns, toolchain, and SLO maturity.
End-to-end deployment of the Reflexion Engine in your environment. Actor/Critic agents tuned to your specific incident patterns and SLOs.
Reduce your AI infrastructure spend by 40-60% and validate your security posture. Mathematical rightsizing, not guesswork.
ChirpStack LLP
The name "ChirpStack" is a nod to LoRa's Chirp Spread Spectrum modulation — a small, distinct signal that cuts through noise and travels vast distances. That's our engineering philosophy: precise signals over noisy abstractions. We build infrastructure that works the way radio physics works — reliably, at range, under real-world conditions.
Production-ready infrastructure. Not flashy — robust. Every release is built to run in environments where downtime has consequences.
Building in public. Our tools are open source because infrastructure shouldn't be a black box. Contributors and testers welcome.
Technical accuracy over marketing language. Engineers are skeptical of vague claims — we speak in benchmarks, not buzzwords.
No vendor lock-in. Our architecture avoids proprietary traps — swap components, fork the code, run it anywhere.
Open Source Projects
Pre-flight Kubernetes manifest scanning powered by Pluto, Polaris, kube-score, and OSV.dev. Catches deprecated APIs, security misconfigurations, and known CVEs before they reach your cluster. Integrates into CI/CD pipelines and MCP-enabled agent workflows.
Model Context Protocol bridge for Helm and Kubernetes. Gives AI agents structured, real-time access to cluster state, Helm releases, and resource topology — enabling agents to reason over live infrastructure instead of stale docs.
Curated Feed
The stories shaping AI infrastructure — from the sources that matter.
Get In Touch
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