AI Agents for Enterprise LLM Observability | $99/mo

Your LLM Chains Are Burning
$42,000/mo in Silent Failures.

A team of specialized AI Agents answers every trace, scores hallucinations inline, attributes token costs by customer, and detects prompt drift — orchestrated by one Main AI Agent for consolidated reports and OpenTelemetry sync. From $99/month. No MLOps engineers. Live in 24 hours.

100 free test credits • Plans from $99/month • Live in 24 hours • SOC 2 & HIPAA Safeguards
Book a Free Demo Start Free — $99/mo →

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ML
OP
CI
Trusted by 180+ AI engineering teams across 24 countries
$99/mo
AI Agent cost vs $180k MLOps engineer
99.2%
Silent hallucination & prompt drift catch rate
< 12ms
Ultra-low tracing latency overhead
$42K+
Average monthly token & GPU waste saved
Silent Production Leaks

4 Things Killing Your LLM Applications Right Now

Every one of these is happening in your production generative AI pipelines today. All four are solved by specialized AI Agents coordinated by one Main AI Agent.

68%
Silent hallucinations & prompt injection leaks
LLMs invent facts, disclose sensitive system prompts, or hallucinate pricing. Without real-time semantic guardrails, flawed outputs reach customers before engineers even notice.
3.4x
Unmonitored token spend & recursive agent loops
A multi-step agent gets trapped in an infinite reasoning loop, generating 50k tokens per query. Your cloud API bill surges 340% overnight without warning.
$180K
Annual MLOps engineer salary & cluster maintenance
Engineering teams spend months trying to stitch together OpenTelemetry collectors, vector DB evaluators, and Prometheus dashboards instead of shipping product features.
4.2s
Unexplained latency spikes across RAG retrieval chains
Users abandon slow chat interfaces when response times jump from 800ms to 5 seconds. Pinpointing which vector lookup or embedding step caused the bottleneck takes hours.
The Fix

Multiple Specialized AI Agents. One Main AI Agent.

A network of specialized AI agents — Guardrail, Cost Auditor, Drift Radar, and Trace Investigator — each executing their domain, while one Main AI Agent coordinates them all, handles reports, and syncs OpenTelemetry 24/7.

Without AI Agent
Model hallucinates a 50% discount to an enterprise buyer. You only discover the error weeks later when customer success escalates an angry email. Deal terms ruined.
With AI Agent
Sub-15ms inline factuality verification. AI Agent cross-references vector context, scores groundedness, and blocks or re-prompts ungrounded claims before the response streams to the user.
99.2% of hallucinations blocked
Without AI Agent
A customer inputs a malicious jailbreak prompt. The model dumps its entire system instructions and internal API keys on screen. Massive security incident.
With AI Agent
Autonomous prompt injection defense. AI Agent intercepts semantic jailbreak vectors and PII disclosures instantly, returning safe fallback guidance and alerting the security team on Slack.
Zero prompt injection escapes
Without AI Agent
An engineer deploys an updated prompt template on Friday. By Monday, retrieval accuracy has dropped 35%, but no one knows which commit caused the regression.
With AI Agent
Continuous semantic prompt drift tracking. AI Agent clusters real-time embeddings, detects quality drop within 50 queries, and provides automated 1-click prompt rollback.
Instant regression detection
Without AI Agent
Enterprise observability suite costs $3,000/month plus $180k MLOps engineer. Setup takes 3 months of wrestling with complex Kubernetes sidecars and Helm charts.
With AI Agent
$99/month. 1-line Python / TypeScript SDK integration. Out-of-the-box support for LangChain, LlamaIndex, vLLM, and LiteLLM. Live in production in under 24 hours.
Saves $178,800/yr vs full-time MLOps
See the Observability Agent Live — Free 30-Min Demo

Join 180+ engineering teams already using RhinoAgents  ·  Next demo slot: Today

Zero Infrastructure Slog

Connect. Configure. Test. Get Work Done.

No Kubernetes clusters. No complex Prometheus configurations. Your AI Observability Agent is live in 24 hours.

Step 01
Connect Your Models & Frameworks
1-line SDK wrapper or OpenTelemetry endpoint for OpenAI, Anthropic, Bedrock, vLLM, LangChain, and LlamaIndex. Zero code refactoring.
OpenAI / Claude LangChain LlamaIndex vLLM / LiteLLM
Step 02
Configure Guardrails & Budgets
Define your evaluation rules in plain English — hallucination tolerance, token rate limits per tenant, PII redacting, and manager approval gates.
Hallucination Caps Token Budgets PII Masking Circuit Breakers
Step 03
Test in a Live Evaluation Sandbox
Replay historic production queries, stress-test prompt injection resistance, and audit multi-hop latency spans before shipping live.
Trace Simulator Eval Datasets Drift Replay Zero Risk
Step 04
Direct Operations via Chat
Ask your Main AI Agent for token cost breakdowns, investigate latency spikes, and approve prompt rollbacks directly in Slack, Teams, or Webchat.
Slack Command Daily Briefings 1-Click Rollback Prompt Ops
No custom MLOps engineering required. Connect your framework via OpenTelemetry, set your evaluation guardrails in plain English, and your Observability Agent is live in under 24 hours.
Multiple Specialized AI Agents · Orchestrated by One Main AI Agent

Multiple Specialized AI Agents.
One Main AI Agent Handles All Reports & Operations.

Each specialized AI agent executes its domain — Guardrails, Cost Attribution, Drift Radar, and Tracing. One Main AI Agent coordinates them all, handles consolidated enterprise reporting, takes your prompts, and syncs OpenTelemetry in real time.

Main Observability Orchestrator
Reports, prompts, incident triage & cross-agent coordination
Guardrail & Hallucination Agent
Real-time factuality, RAG groundedness & PII redacting
Token & Cost Auditor Agent
Per-customer attribution, loop circuit breaker & cache optimization
Prompt Drift & Eval Agent
Semantic drift clustering, regression alerts & 1-click rollbacks
Latency & Trace Investigator
Distributed spans, vector DB latency & bottleneck diagnostics
Get Reports via Prompt
"@Rhino token audit: Show 7d spend by customer with > 20% surge"
No digging through complex Grafana dashboards. Ask for token spend by model, P99 latency trends, or hallucination rates.
Assign Evals via Prompt
"Evaluate v2 customer support prompt against 500 historic queries"
Delegate continuous evaluation suites, synthetic test generation, or root-cause bottleneck investigations to specialized sub-agents.
Schedule Audits via Prompt
"Schedule daily 9 AM Slack report of ungrounded responses and drift alerts"
Set automated daily briefings, weekly model drift audits, and real-time Slack incident notifications in plain English.
Update Guardrails via Prompt
"Roll back customer support system prompt to #v2.4 and lower hallucination limit"
Modify production guardrails, adjust circuit breaker limits, or roll back prompt versions instantly without redeploying code.
OpenTelemetry / OTLP
Distributed traces & spans
LangChain & LlamaIndex
Framework hooks & callbacks
vLLM & LiteLLM Proxy
Model gateway & circuit breaker
Slack & Teams
Team prompts & incident triage
Datadog & Grafana
Metrics export & dashboards
What Engineers Do in Observability Console
Autonomous Loop Circuit Breakers
Recursive agent loop detection (halts runaway ReAct chains at iteration N)
Automated tool call deduplication & dead-loop prevention
Graceful fallback response injection with zero user interruption
Instant Slack war-room incident dispatch with trace flamegraph link
Inline Groundedness & PII Guardrails
Sub-15ms semantic factuality verification against RAG vector contexts
Automated PII masking (SSN, credit card, API keys, patient PHI)
Adversarial prompt injection & jailbreak interception
Zero data retention mode for HIPAA & SOC 2 strict compliance
Embedding Drift Radar & Versioning
Semantic embedding radar — tracks centroid shift vs gold-standard benchmarks
1-Click Prompt Rollback — revert bad templates through Slack in 2 seconds
Tenant Token Attribution — per-customer cost accounting and hard budget caps
Click Scenario to View Chat:
Interactive Demo
RhinoAgent — AI Observability Engine
Production Cluster · OpenTelemetry & LangChain Connected
Rhino Observability Orchestrator online. Monitoring live production traces across 4 model endpoints.
You
Alert: Trace #TR-9941 has triggered 8 sequential recursive LLM calls in 12 seconds. Investigate immediately.
On it! Triggering Token Cost Auditor & Latency Investigator...
// Executing autonomous circuit breaker & span diagnosis...
Detected infinite reasoning loop in Customer Refund Agent (Repeated tool call: check_order_status)
Token consumption: 48,200 tokens consumed ($1.44 burn in 12s)
Circuit breaker action: Autonomous execution halt triggered at iteration 8
Incident logged in Slack #ai-incidents · Staged safe graceful fallback response to user
You
Halt confirmed. Update circuit breaker threshold to 5 maximum tool calls for all refund sub-agents.
✅ Circuit breaker rule updated in production gateway: max_tool_iterations = 5 for sub-agent group refund-v2.

Trace #TR-9941 archived with full OpenTelemetry span flamegraph. Pushed incident postmortem draft to Confluence and notified on-call engineering.
Reports Generated Through Prompt Only

Know Exactly What Your LLM Pipeline Did. Every Day.

Daily reliability briefings, token cost breakdowns by customer, and prompt drift alerts — delivered to your Slack, email, or dashboard automatically or on-demand via prompt.

Daily Reliability & Drift Briefing
Sent at 8 AM or requested anytime: total production traces analyzed, hallucination block rate, prompt drift alerts, and pending approvals.
Traces analyzed (24h)1,248,400
Hallucinations blocked inline384
Semantic drift warnings2
Prompt rollbacks recommended1
Token Economics & GPU Attribution
Tracks token consumption across models (GPT-4o, Claude 3.5, Llama 3), caching hit ratios, and cost allocation per enterprise tenant.
Total tokens monitored84.2M
Semantic cache hit rate42%
Unnecessary loop spend prevented$3,420
Cost per 1k queries$0.42
ROI & MLOps Savings Statement
Monthly executive breakdown: total cloud token savings, MLOps engineering hours saved vs $99/mo, and overall generative AI gross margin impact.
Token & cloud waste saved$42,800/mo
MLOps payroll saved$15,000/mo
RhinoAgents platform fee$99/mo
Net monthly financial ROI582x
Multiple Specialized AI Agents · One Main AI Agent

One Main AI Agent Handles All Reports.
Specialized AI Agents Do the Specific Work.

Leadership prompts the Main AI Agent for consolidated reliability reports, token cost briefings, and approvals. Each engineering persona interacts with specialized sub-agents tailored to their workflow in Slack, Teams, or Webchat.

Executive AI Leadership
Consolidated Reliability Reports & Full Pipeline Oversight.
The VP of AI chats directly with the Main Observability Orchestrator Agent. It consolidates live metrics from the Guardrail, Cost, Drift, and Tracing sub-agents into unified morning executive summaries, flags budget surges, and ensures enterprise SLAs.
Consolidated Model SLA Briefing via Chat
Main AI Agent queries all observability sub-agents — delivering a complete pipeline uptime, cost, and reliability summary to Slack every morning at 8 AM.
Cross-Model Budget & Safety Gates
Define autonomous thresholds (queries < $0.05) vs. high-dollar anomaly requests requiring 1-click VP Slack approval.
Complete Production Audit Trail
Every trace span, prompt template version, evaluation score, and model response indexed into one searchable audit log.
#ai-leadership · Main Observability Orchestrator
Multi-Agent Reporting Hub
VP
Elena Rostova (VP of AI)8:02 AM
@Rhino morning briefing: Give me our 24-hour summary — total traces, token spend by provider, P99 latency across RAG pipelines, and any safety violations.
Main Observability Orchestrator8:02 AM
Good morning Elena! Here is your consolidated 24-hour production LLM briefing:

📊 Production Scale: 1,248,400 traces processed · 99.98% pipeline uptime.
💰 Token Spend: $1,842 total ($1,100 OpenAI · $520 Anthropic · $222 self-hosted vLLM).
⏱️ P99 Latency: 820ms (RAG vector retrieval: 140ms · LLM generation: 680ms).
🛡️ Safety & Guardrails: 14 prompt injection attempts intercepted & neutralized. Zero PII disclosures.

⚠️ Action Item: Account GlobalFin token burn increased 38% due to a poorly formatted retry loop. Cost Auditor Agent temporarily throttled requests to prevent overages.
Cloud Gateway & Quotas
Token & Cost Auditor Agent: Cloud Gateway & Quotas.
The MLOps Lead commands the specialized Token Cost & Gateway AI Agent to allocate per-tenant budgets, enforce circuit-breaker retry rules, optimize semantic caching, and diagnose runaway GPU nodes.
Per-Tenant Token Quota Defense
Enforces hard token consumption limits per customer API key, preventing any single tenant from monopolizing GPU clusters.
Semantic Response Caching
Caches recurring semantic queries at the gateway, eliminating redundant LLM API calls and cutting monthly token bills by 40%+.
Automated Fallback Model Routing
If Claude 3.5 Sonnet encounters rate limits or 503 errors, the agent reroutes traffic automatically to GPT-4o in under 50ms.
#mlops-war-room · Token Cost & Gateway Agent
Cost & Routing Hub
ML
Marcus Vance (Lead MLOps)10:14 AM
@Rhino Cost Auditor: Show our semantic cache hit rate across customer support queries this week and calculate total dollars saved.
Token Cost & Gateway Agent10:14 AM
⚡ Semantic Caching Audit (Last 7 Days):

• Total Inbound Queries: 412,000 requests.
• Cache Hits: 181,280 queries served directly from Redis vector cache (44.0% hit rate).
• Average Cache Latency: 14ms (vs 1,120ms full LLM generation).
• Tokens Saved: 72.5M tokens prevented from upstream model call.
• Direct Cost Savings: $1,812.50 saved this week ($7,250/mo projected).
Evals & Versioning
Prompt Drift & Evals Agent: Versioning & Quality.
The AI Product Lead uses the specialized Prompt Drift & Eval AI Agent to measure semantic output variance, run continuous synthetic benchmarks, and execute zero-downtime prompt rollbacks.
Continuous Semantic Embedding Drift
Monitors production response embeddings against gold-standard evaluation benchmarks to catch subtle quality degradation early.
LLM-as-a-Judge Automated Evals
Scores output quality, tone, conciseness, and answer completeness on a 1-5 scale across 100% of production interactions.
1-Click Prompt Rollback
If a prompt tweak causes unexpected regressions, roll back to any previous version instantly through Slack without opening a git PR.
#prompt-engineering · Prompt Drift & Eval Agent
Eval & Drift Radar
PM
Sofia Chen (AI Product Manager)2:30 PM
@Rhino Drift Agent: We deployed prompt template #v3.1 for customer support 30 minutes ago. Are we seeing any semantic drift or eval regressions?
Prompt Drift & Eval Agent2:30 PM
⚠️ Drift Warning Detected on Prompt #v3.1 (120 queries evaluated):

• Semantic Cosine Distance: Shifted by 0.18 away from gold standard cluster.
• Hallucination Rate: Increased from 0.4% → 2.8% (Model is fabricating return policy details).
• Tone Score: 4.8/5.0 (Polite, but factually inaccurate).
• Recommendation: Roll back to prompt template #v3.0.

Enterprise Security & PII
Guardrail & Hallucination Agent: Enterprise Security & PII.
The CISO relies on the specialized Guardrail & Hallucination AI Agent to inspect inbound prompts for jailbreak attempts, redact customer PII in real time, and guarantee SOC 2 & HIPAA compliance.
Autonomous Prompt Injection Shield
Neutralizes direct and indirect prompt injection attacks, system prompt extraction, and adversarial jailbreaks in under 15ms.
Real-Time PII Anonymization
Redacts Social Security Numbers, credit card details, API keys, and healthcare PHI before prompts are dispatched to third-party models.
SOC 2 & HIPAA Continuous Compliance
Automated evidence logging of all safety boundary enforcements, with zero data retention options for sensitive healthcare data.
#security-soc · Guardrail & Hallucination Agent
Security & PII Shield
SO
Rachel Green (CISO)4:10 PM
@Rhino Security: Audit all prompt injection attacks intercepted across our public web chat API in the last 24 hours.
Guardrail & Hallucination Agent4:10 PM
🛡️ Security Audit (Last 24 Hours):

• Attacks Intercepted: 18 malicious injection payloads blocked.
• Attack Vectors: 11 system prompt leak requests · 5 roleplay jailbreak wrappers · 2 base64 encoded injection payloads.
• Attacker IP: 14 attempts originated from single ASN in Eastern Europe (Automatically IP rate-limited).
• PII Scrubbed: 42 user phone numbers and 6 credit card sequences masked prior to model ingestion.
• Compliance Status: 100% compliant with SOC 2 Type II trust criteria.
Full Platform

Every Feature Your AI Engineering Team Needs. Built In.

Not a static dashboard. A full autonomous AI Observability platform with real-time guardrails, cost controls, trace visualizations, and enterprise RBAC.

Real-Time Hallucination & Groundedness Scoring
Calculates semantic overlap between RAG context and model completions, blocking or re-prompting ungrounded claims in sub-15ms.
RAG EvaluationSub-15msFactuality
Automated Prompt Drift Detection & Rollback
Clusters production embeddings across prompt versions, alerting engineers to quality degradation and enabling 1-click Slack rollbacks.
Embedding Drift1-Click RollbackVersion Control
Multi-Hop Distributed Trace Flamegraphs
Visualizes every agent tool call, embedding query, vector similarity search, and model completion in an interactive OpenTelemetry trace timeline.
OpenTelemetryTrace FlamegraphVector DB Spans
Per-Tenant Token Attribution & Budget Caps
Tracks token consumption down to individual user IDs and customer organizations, enforcing automated circuit-breaker hard caps.
Customer AttributionCircuit BreakersQuota Alerts
PII Masking & Prompt Injection Firewall
Intercepts adversarial jailbreak attempts and anonymizes Social Security Numbers, emails, and credit cards before queries leave your VPC.
Jailbreak DefensePII RedactionHIPAA Ready
Automated Continuous LLM-as-a-Judge Evals
Runs automated evaluation judges across 100% of production traffic, scoring conciseness, relevance, toxicity, and compliance continuously.
LLM Judge100% TrafficSynthetic Tests
Integrations

Connects to Everything in Your AI Stack.

400+ pre-built integrations. 1-line SDK installation. Your Observability Agent slots into your existing stack — no infrastructure refactoring required.

FAQ

Common Questions About AI Observability Agents

Traditional APM tools like Datadog track server CPU and latency, but have zero semantic understanding of hallucination, prompt drift, or ungrounded claims. Standalone logging tools like LangSmith provide passive dashboards, but require human engineers to constantly inspect traces. RhinoAgents deploys an autonomous multi-agent observability workforce: it scores hallucinations in real time, breaks runaway agent loops automatically, detects semantic drift, and executes prompt rollbacks through conversational prompts.

Under 12ms. RhinoAgents uses asynchronous non-blocking OpenTelemetry instrumentation for distributed tracing and token counting, meaning your primary application response stream is never slowed down. For inline safety guardrails (prompt injection and PII masking), our lightweight Rust-based sidecar evaluation engine executes in under 15ms.

Recursive AI agents (like ReAct frameworks) can enter infinite reasoning loops when tool calls return unexpected errors. The Token Cost Auditor monitors sequential tool iterations in real time. If an agent repeats identical tool calls or exceeds your configured iteration threshold (e.g. 5 steps), the circuit breaker halts execution, returns a graceful fallback message to the user, and alerts your on-call engineering team on Slack.

Yes, 100%. We enforce enterprise-grade security. RhinoAgents is SOC 2 Type II and HIPAA compliant. All traces and embeddings are encrypted using AES-256 at rest and TLS 1.3 in transit. You can deploy our lightweight agent proxy inside your own private VPC (AWS, GCP, Azure), ensuring customer data and proprietary prompts never leave your secure perimeter.

The flat $99/mo plan includes the Main Observability Orchestrator and all 4 specialized sub-agents (Guardrail Agent, Cost Auditor, Drift Sentinel, and Trace Investigator). You receive 100 free test operational credits, pre-built LangChain/vLLM/OpenTelemetry connectors, zero setup fees, and can cancel anytime. See full pricing →

"
"RhinoAgents caught a recursive agent loop that would have burned $14,000 in OpenAI API credits in a single weekend. Having one Main Agent summarize our latency, cost, and hallucination rates in Slack every morning replaced weeks of custom MLOps engineering."
Vikram Desai — Head of AI Platform, SynthScale Systems
AI Observability for Enterprise Teams

Connect. Configure. Get Work Done.
Plans from $99/mo.

Your AI Observability Agent connects in minutes, evaluates live production traces, blocks hallucinations, and protects your token budget 24/7. Cancel anytime.

Book a Free Demo Start Free — $99/mo →
100 free test credits • No credit card required • Cancel anytime • Live in 24 hours
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