Your Infrastructure Fixes Itself Before You Know It's Broken
Nexus-Alive is a multi-agent system that continuously watches your infrastructure, predicts failures up to 60 minutes before they occur, and autonomously remediates them — with multi-model consensus before any high-impact action is taken.
Five Specialized Agent Teams
Each team is purpose-built for one phase of the detect → predict → heal → learn cycle. Together they form a closed loop that improves with every incident.
Discovery Team
Four watchers run continuously on 30-second health check cycles — inspecting container state, pod readiness, deployment history, and service endpoints. Every anomaly is emitted as a structured event with full context attached.
Analysis Team
Three analysis agents combine signals from three independent sources to produce a failure probability score. Historical patterns contribute 40%, recognized failure signatures 35%, and trend extrapolation 25%. The composite score drives remediation priority.
Healing Team
When the consensus vote clears, the Healing team executes the remediation plan in a sequence of atomic steps — each with a rollback checkpoint. If any step fails validation, the RollbackGuard restores the last known good state before surfacing the failure.
Consensus Team
Three independent language models review the proposed remediation plan and vote. A 2-of-3 majority is required before any action executes. No single model's reasoning can override the group — and all three reasoning chains are stored in GraphRAG for auditability.
Learning Team
After every resolved incident, PatternMiner extracts a reusable remediation pattern and writes it to GraphRAG. The next time a similar failure emerges, the Analysis team finds a proven solution — not a novel hypothesis. The system gets faster and more accurate with each cycle.
Three Sources. One Probability Score.
No single signal is reliable enough to act on. Nexus-Alive combines three independent analytical streams and weights them by proven predictive accuracy.
Historical Patterns
Failure sequences observed in past incidents, stored in GraphRAG and matched against current telemetry. The longest-established predictor — high confidence, lower sensitivity to novel failure modes.
Failure Signatures
Known precursor signatures — specific metric combinations that reliably precede failure. Updated by PatternMiner after every incident. Highest precision when a recognized pattern matches.
Trend Extrapolation
Short-window trend analysis on memory, CPU, latency, and error rate metrics. Catches novel failure modes that have no historical precedent — lower confidence, but high coverage.
Weights are re-calibrated quarterly against realized outcomes across all production deployments.
No Single Model Decides Alone
Three independent language models review every proposed remediation. A 2-of-3 majority is required. All reasoning is stored and auditable.
Autonomous Where Safe. Gated Where It Matters.
Every proposed action is classified before execution. The classification determines the approval mode — not the on-call engineer's availability at 3am.
| Risk Level | Example Actions | Approval Mode | Consensus Required |
|---|---|---|---|
| LOW | Restart crashed container, clear temp files, update config value | Fully autonomous | Simple majority |
| MEDIUM | Scale deployment, rotate secrets, modify resource limits | Auto + config change | 2-of-3 + confidence ≥ 0.8 |
| HIGH | Database failover, cluster node replacement, rollback deployment | Human approval required | 2-of-3 + human sign-off |
| CRITICAL | Data migration, production schema change, cross-region failover | Never autonomous | Full human decision |
Infrastructure That Heals Itself
See Nexus-Alive predict and resolve a live failure in a demo against your infrastructure configuration.