Four integrated pillars that give you full visibility, intelligent detection, cost control, and automated governance across your entire AI agent ecosystem.
Automatically scan your infrastructure to discover every AI agent — including shadow agents deployed without IT oversight. Maintain a living, searchable registry with real-time health monitoring.
Continuous discovery across cloud environments, APIs, and internal services
Identify unauthorized or forgotten agents operating in your environment
Real-time status, uptime, and performance metrics for every registered agent
Unified metadata regardless of whether agents use LangChain, CrewAI, AutoGen, or custom frameworks
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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