The Atruvo
Platform Failure Modes Index.

Uptime, latency, and error rate look healthy. Nobody is measuring whether the system is producing the correct result.

P99 latency or accuracy slowly drifts over weeks. No threshold trips. SLO budget consumed before anyone notices.

Nobody can answer 'what else fails if this fails?' until the incident is already in progress.

Dashboard reports 99.95% attainment. Customers file tickets. Definition creep, aggregation, and synthetic monitoring hide the truth.

AI-authored code makes deployment frequency and lead time look favourable on the dashboard. The bottleneck moves to verification capacity, review queue depth by seniority, and rollback rate — places DORA does not measure.

The infra layer is monitored. Output quality and drift are not. AI degradation is invisible until customers report it.

Data, ML, and Platform teams each confirm their part is green. The pipeline between them is broken and unowned.

Board approved roadmap, vendor selection, and budget. No on-call, no rollback plan, no cost controls, no fallback.

Team is staffed with researchers who cannot ship to production, or platform engineers without AI context.

Compliance, audit trail, and rollback are treated as policy documents, not platform features. Nobody operationally owns them.

Architecture diagrams show critical services. On-call data shows different services pay the reliability cost. Engineering time is invested against the wrong systems.

Only one engineer can deploy to a given environment. Tribal Ansible, undocumented credentials, named-person approval gates. Deployments wait when that person is on leave.

Business case focused on model and GPU costs. The data pipeline is 60-70% of total spend and nobody budgeted for it.

GPU, LLM API calls, and embedding jobs buried in 'general compute' line items. Nobody can attribute cost to a specific AI feature.

Platform is running at 80% of an unknown ceiling. Adding AI workload pushes it over. Discovery happens during incident.

Token consumption controlled per request, not per agent run. One stuck tool call burns a month of FinOps headroom in an hour. No max-iterations circuit breaker.

AI bolted onto a platform with unknown cache layers, undocumented queues, and engineers on leave. Hidden failure modes detonate at week 8.

Cluster designed for CPU autoscaling cannot serve GPU AI workloads. Scaling takes 5 minutes. Cold start kills inference SLOs.

Vector DB latency, stale embeddings, and retrieval drift all break differently from dev to prod. Each one looks like a model problem.

Migration stalled at 60% for 18 months to 4 years. The last 40% is a different project than the first 60%. Reporting tracks against a plan that no longer reflects reality.

Multi-cloud strategy doubles platform cost without delivering the promised resilience. Adopted as a hedge against indecision, not a real risk.

Platform built for stateless HTTP cannot host non-deterministic, long-lived, tool-invoking agent workloads. No token budget per run, no eval floor, no gateway SLO. Agents stall before reaching production.

IDP measures catalog coverage rather than developer outcomes. Adoption sits at 10% because developers correctly route around it. AI agents route around it too because it was built for humans clicking a UI, not machines invoking structured tools.

When the model fails, the system silently returns cached or default results. Errors are hidden for weeks before customers notice.

Rolling back a model leaves embeddings, stored decisions, and downstream state from the new model in place. System breaks differently.

Guardrails pass 100% in dev. In production they let through 28% of wrong-but-well-formed answers because they never checked correctness.

Each application implements its own regex filter. One gap exposes the entire org. The defence belongs at the platform gateway.

Inputs, outputs, model version, and confidence scores not stored. Compliance audit cannot prove what the model saw or returned.

Strategy, governance, and policy all exist on paper. The platform cannot produce the evidence the audit will demand.

CISO writes correct policy. Platform has no AI gateway to enforce it. Each team implements badly. CISO blocks rollouts because policy is unenforceable.