Full-stack visibility into every AI request, agent action, token consumed, and dollar spent — built into the ibl.ai Operating System.
When AI runs at scale across hundreds of teams and thousands of users, visibility is not optional. ibl.ai provides a production-grade observability stack baked directly into the AI Operating System — not bolted on as an afterthought.
Every request, every model call, every agent reasoning step, and every tool execution is traced, measured, and surfaced in real time. From token consumption and latency percentiles to cost attribution and error rates, you have the instrumentation you need to operate AI like infrastructure.
Compatible with Grafana and Prometheus, ibl.ai's observability layer integrates into your existing monitoring stack. Whether you run on-premise, in a private cloud, or across a hybrid environment, you own the data and the dashboards.
Most organizations deploying AI have no reliable way to answer basic operational questions: Which models are being called? How much is each department spending on tokens? Why did that agent fail at 2 AM? Without a dedicated observability layer, AI operations are a black box — teams discover problems only after users complain or bills arrive.
As AI scales from a pilot to production infrastructure serving thousands of users, the absence of proper monitoring creates compounding risk. Performance regressions go undetected, runaway costs accumulate silently, security anomalies are missed, and engineering teams spend hours debugging failures they could have prevented. AI without observability is not production-grade — it is a liability.
LLM API costs accumulate across dozens of models, agents, and user groups with no unified view of who is spending what.
Finance teams receive unexpected invoices. Engineering has no data to optimize model routing or enforce budgets.Autonomous agents fail mid-task — tool calls time out, reasoning loops stall, external APIs return errors — with no alerting or audit trail.
Users receive degraded or incorrect outputs. Failures are discovered reactively, long after the damage is done.Without per-request tracing, teams cannot identify which model, skill, or integration step is introducing latency into the user experience.
SLA breaches go undetected. Optimization efforts are guesswork rather than data-driven decisions.Anomalous usage patterns — prompt injection attempts, credential abuse, unusual data access — are not surfaced without purpose-built AI security monitoring.
Compliance audits fail. Security incidents are discovered weeks late, after data has been exposed or policies violated.Teams stitch together logging from individual LLM providers, custom scripts, and generic APM tools that were never designed for agentic AI workloads.
Observability coverage is incomplete, inconsistent, and expensive to maintain as the AI stack evolves.Every component of the ibl.ai OS — Agent Runtime, Model Router, Gateway, Orchestrator, Memory Layer — emits structured telemetry automatically. No manual instrumentation required.
Each inbound request receives a distributed trace ID that follows it through model routing, agent reasoning steps, tool calls, memory lookups, and final response delivery.
Token usage, latency, error rates, and cost are aggregated per request, per agent, per user, per tenant, and per model. Cost attribution is available at department or project granularity.
Configurable alert rules fire on performance degradation, error rate spikes, cost threshold breaches, unusual access patterns, and security events. Alerts route to PagerDuty, Slack, email, or webhooks.
All metrics are exposed via a Prometheus-compatible endpoint. Pre-built Grafana dashboards ship with the platform. Teams can extend, customize, or integrate with existing observability stacks.
Immutable audit logs capture every agent action, data access event, and model call with user identity, timestamp, and policy context — ready for HIPAA, FERPA, SOX, and FedRAMP audits.
End-to-end trace visibility from user input through model routing, agent execution, tool calls, and response delivery. Identify exactly where latency or failures originate across the full AI stack.
Real-time and historical dashboards showing token consumption and cost broken down by model, agent, user, department, and tenant. Set budget alerts before costs become surprises.
P50, P95, and P99 latency metrics per model, per agent skill, and per integration endpoint. Track performance trends over time and detect regressions before users notice.
Aggregate and per-component error rates with structured error context. Drill from a dashboard spike directly into the trace that caused it for rapid root cause identification.
Behavioral baselines detect unusual usage patterns, prompt injection attempts, credential misuse, and unauthorized data access. Security events are surfaced in real time with full context.
Native Prometheus metrics endpoint and pre-built Grafana dashboard templates. Plug ibl.ai observability data directly into your existing monitoring infrastructure without migration or lock-in.
Each tenant organization sees only its own telemetry. Platform operators get a unified cross-tenant view. Data isolation is enforced at the infrastructure level, not the application layer.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Cost Visibility | Token costs aggregated at the provider level only. No breakdown by team, agent, or use case. Finance surprises every billing cycle. | Real-time cost dashboards with attribution by model, agent, user, department, and tenant. Budget alerts fire before thresholds are breached. |
| Failure Detection | Agent failures discovered when users report problems. No structured error context. Debugging requires manual log archaeology. | Error rate alerts fire in real time. Distributed traces link every failure to the exact component, model call, or tool execution that caused it. |
| Latency Insight | End-to-end response time is the only metric available. No visibility into which model, skill, or integration step is the bottleneck. | Per-component latency at P50/P95/P99. Trace waterfall views show exactly where time is spent across the full request lifecycle. |
| Security Monitoring | No behavioral baselines for AI usage. Prompt injection attempts, credential misuse, and unauthorized data access go undetected. | Anomaly detection surfaces security events in real time. Immutable audit logs provide evidence for incident response and compliance audits. |
| Compliance Readiness | Audit evidence must be assembled manually from fragmented logs across multiple systems. Compliance audits are expensive and time-consuming. | Structured, immutable audit logs are generated automatically. HIPAA, FERPA, SOX, and FedRAMP evidence packages are exportable on demand. |
| Tooling Integration | Custom scripts and generic APM tools provide partial coverage. Maintaining observability across a growing AI stack requires ongoing engineering effort. | Native Prometheus and Grafana compatibility. Plugs into existing monitoring infrastructure. No custom instrumentation required. |
| Multi-Tenant Visibility | No isolation between tenant observability data. Platform operators cannot get a unified cross-tenant view without building custom tooling. | Tenants see only their own telemetry. Operators get a unified cross-tenant dashboard. Isolation enforced at the infrastructure layer. |
Token costs aggregated at the provider level only. No breakdown by team, agent, or use case. Finance surprises every billing cycle.
Real-time cost dashboards with attribution by model, agent, user, department, and tenant. Budget alerts fire before thresholds are breached.
Agent failures discovered when users report problems. No structured error context. Debugging requires manual log archaeology.
Error rate alerts fire in real time. Distributed traces link every failure to the exact component, model call, or tool execution that caused it.
End-to-end response time is the only metric available. No visibility into which model, skill, or integration step is the bottleneck.
Per-component latency at P50/P95/P99. Trace waterfall views show exactly where time is spent across the full request lifecycle.
No behavioral baselines for AI usage. Prompt injection attempts, credential misuse, and unauthorized data access go undetected.
Anomaly detection surfaces security events in real time. Immutable audit logs provide evidence for incident response and compliance audits.
Audit evidence must be assembled manually from fragmented logs across multiple systems. Compliance audits are expensive and time-consuming.
Structured, immutable audit logs are generated automatically. HIPAA, FERPA, SOX, and FedRAMP evidence packages are exportable on demand.
Custom scripts and generic APM tools provide partial coverage. Maintaining observability across a growing AI stack requires ongoing engineering effort.
Native Prometheus and Grafana compatibility. Plugs into existing monitoring infrastructure. No custom instrumentation required.
No isolation between tenant observability data. Platform operators cannot get a unified cross-tenant view without building custom tooling.
Tenants see only their own telemetry. Operators get a unified cross-tenant dashboard. Isolation enforced at the infrastructure layer.
Engineering teams gain operational confidence to scale AI from pilot to enterprise-wide deployment without losing visibility.
Platform operators allocate AI budgets accurately and detect unusual usage patterns that may indicate policy violations.
Compliance teams have audit-ready evidence. Security teams detect anomalies before they become reportable incidents.
Regulatory obligations are met without custom compliance tooling. SLA breaches are caught in real time, not in post-mortems.
Agencies deploy AI with the governance controls required for public sector accountability and security compliance.
Startups avoid runaway AI spend and build operational maturity into their AI stack before scaling creates unmanageable complexity.
Regulated organizations deploy AI without sacrificing the documentation and oversight their compliance frameworks demand.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.