Four integrated pillars that give you full visibility, intelligent detection, cost control, and automated governance across your entire AI agent ecosystem.
Every agent that emits OpenTelemetry to MeshAI enters the registry automatically, whether or not anyone registered it. Point your OTel Collector at MeshAI once, and each agent any team deploys inside that pipeline shows up on its own, with framework, model, cost, and behavior attached. The result is a living, searchable registry with no per-agent onboarding.
Agents enter the registry the first time they emit telemetry. No manual onboarding, no per-agent setup.
Agents that appear in telemetry but were never declared are tagged for review, so sprawl inside your pipeline surfaces on its own.
Real-time status, uptime, and performance metrics for every registered agent
Unified metadata regardless of whether agents use LangChain, CrewAI, AutoGen, or custom frameworks
Discovery covers everything in your OpenTelemetry pipeline. MeshAI installs no endpoint agents and scans no networks.
Four detection algorithms analyze every agent's behavior, costs, reliability, and security patterns every 5 minutes. Configurable alert rules with webhook delivery for Slack, PagerDuty, and custom endpoints.
Z-score analysis against 24-hour rolling baseline detects unusual token spend. Absolute multiplier catches sudden 5x+ cost jumps.
Monitors error rates and P95 latency against 7-day baselines. Alerts when error rate exceeds 2x normal or 25% absolute.
Detects when agents start using models not seen in the past 7 days, a strong indicator of configuration changes or compromise.
Catches request rate spikes (10x+ normal) and dormant agent reactivation: agents silent for 7+ days suddenly making requests.
Know exactly where every token dollar goes. Attribute costs to teams, projects, and individual agents. Set guardrails and let ML optimize your model routing.
Granular spend tracking by team, project, agent, and individual request
Automatic enforcement with configurable thresholds and escalation policies
Route requests to the most cost-effective model that meets quality requirements
Predict future costs based on usage trends and planned agent deployments
Eight policy types enforced in real-time through the proxy with sub-5ms overhead. Immutable audit trails, EU AI Act readiness scoring (0-100), and HITL approval workflows, all built and deployed.
8 policy types (model allowlist, block provider, require approval, budget limit, rate limit, human review, prompt injection, PII filter) evaluated at the proxy layer
15+ injection patterns scanned on every request. Role override, jailbreak, delimiter attacks, and encoding evasion, blocked before reaching the LLM.
8 PII types detected in LLM responses (email, phone, SSN, credit card, IP, passport, IBAN). Block, redact, or allow with logging: your choice.
Automated 0-100 compliance score across 5 components: audit trail, risk classification, HITL, documentation, data retention
Proxy returns 403 for approval-required policies. Dashboard queue with approve/deny. Redis-cached for instant subsequent access.
AI-assisted risk suggestion from agent metadata with mandatory human confirmation per Article 14. FRIA templates and transparency cards.
Statistical detection runs in real-time on lightweight Cloud Run workers. BigQuery ML powers daily ARIMA cost forecasting. OpenTelemetry ingestion connects any agent framework automatically.
Z-score, rate-of-change, and set-diff algorithms run every 5 minutes on 5-minute metric aggregations. Sub-$30/mo infrastructure cost.
ARIMA_PLUS time-series models trained weekly per agent detect cost anomalies against predicted spend. ML.DETECT_ANOMALIES runs daily.
OTLP/HTTP endpoint auto-discovers OpenClaw and NemoClaw agents from trace data. Zero code changes: one env var to connect.
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See these pillars applied to real-world use cases, read what AI agent observability covers on its own, or see how the runtime layer sits against device, observability, and GRC tooling in the layer-by-layer comparisons.