Run AI Agents Like Production Infrastructure
AI agents in production need the same operational rigor as production services: monitoring, alerting, performance optimization, cost management, and incident response. AgentOps provides the operational layer for managing agent fleets at enterprise scale — with full observability and governed intervention capabilities
The Challenge
AI Agents Are Deployed Like Prototypes and Expected to Run Like Production
Teams build agents, deploy them, and move on. There's no operational framework for monitoring agent health, managing costs, handling failures, or improving performance. When an agent fails at 2am, there's no runbook — because agents don't have ops
No observability for AI decisions
APM tools track latency and errors, but cannot measure decision quality, hallucinations, policy compliance, or authority usage
Standard APM tools ignore AI decision quality completely
Hallucinations and policy violations remain undetected automatically
Authority utilization across agents is invisible without custom tracking
Outcome: Decision quality and agent performance remain unmonitored without specialized observability
Cost management is invisible
Each agent call consumes tokens, API requests, and compute, but usage remains opaque until billing statements arrive
AI usage costs are hidden until end-of-month invoices arrive
Per-agent token and compute consumption is not tracked
Departments lack visibility into spending trends and overages
Outcome: Teams cannot control AI operational costs without active monitoring
No incident response for agent failures
When agents produce incorrect results, there is no automated detection, rollback, or structured incident response procedure
Incorrect decisions go unnoticed without automated alerts or monitoring tools in place
Rollbacks or corrections must be manual and ad hoc
No structured incident playbook exists to guide responses to agent failures
Outcome: Agent failures are reactive, causing delayed responses and operational risk
How It Works
How AgentOps Works
AgentOps provides the complete operational layer for AI agent fleets: real-time monitoring, cost management, performance optimization, and governed intervention
Agent Observability
Real-time monitoring of every agent dimension: decision volume, latency, accuracy, compliance rate, hallucination rate, escalation frequency, and resource consumption. Custom dashboards per team, department, and use case
Cost & Resource Management
Track AI spend at every level: per-token, per-decision, per-agent, per-team, and per-department. Budget alerts, cost allocation, and optimization recommendations. Intelligent model routing to balance quality and cost
Governed Intervention
When agents drift, fail, or exceed thresholds, AgentOps enables governed intervention: automatic rate limiting, model fallback, agent suspension, and human escalation. All interventions are traced
Capabilities
What AgentOps Delivers
AgentOps provides full operational visibility, cost tracking, decision monitoring, and performance optimization for all AI agents in production environments
Real-Time Agent Dashboard
Fleet-wide visibility shows agent count, status, decision volume, accuracy trends, compliance rates, and anomalies across all teams
Drill down from fleet to team to individual agent to monitor operational health and efficiency continuously
Operators gain complete visibility into agent performance and fleet-wide operational status
AI Cost Intelligence
Budgets can be set with automated enforcement while identifying optimization opportunities to reduce unnecessary expenditures
Track every dollar of AI spend including token, API, and compute costs for each team or project
AI costs are monitored, allocated, and optimized across departments and projects
Decision Quality Monitoring
Monitor decision accuracy, hallucination rates, policy compliance, and user satisfaction per agent in real time
Set quality thresholds and trigger alerts automatically when agents drift below acceptable performance standards
Decision quality is tracked and deviations are immediately identified for corrective action
Performance Optimization
AgentOps analyzes operational data to recommend prompt improvements, model changes, context tuning, and caching strategies
Recommendations are implemented in a governed way, ensuring optimizations remain within authority and compliance boundaries
Agent performance is continually enhanced through actionable, data-driven optimization insights
Model Fallback Chains
Agents automatically switch to secondary models while monitoring quality, ensuring uninterrupted operations with minimal impact
Define fallback model sequences for each agent to maintain service continuity during primary model failures
Service continuity is preserved with automatic fallback and quality monitoring
Operational Decision Traces
Every operational action—rate limiting, model switching, or agent suspension—is logged with triggers, actions, and resulting impacts
Decision traces provide a complete, auditable record for governance, troubleshooting, and compliance verification
All operational interventions are fully traceable for auditing and accountability purposes
Use Cases
AgentOps in Action
These real-world examples show how AgentOps detects issues, manages quality, and orchestrates operational workflows with governed execution intelligence
Integrations
Connects to Your Enterprise Stack
ElixirData seamlessly integrates with leading identity providers, secrets management, zero trust, and PAM solutions for robust enterprise security and streamlined access control
Observability
Cost Management
Model Providers
Alerting
FAQ
Frequently Asked Questions
Standard APM tracks HTTP metrics. AgentOps tracks AI decision metrics like accuracy, hallucinations, compliance, authority use, escalations, efficiency, and satisfaction
Every agent decision tracks cost metadata. AgentOps aggregates per-decision costs to per-agent, team, department, and use-case levels with enforceable budgets
Yes. Each agent defines a fallback chain. If a model fails or exceeds thresholds, the agent automatically switches, with monitoring and alerts maintained
AgentOps enables governed canary rollouts: gradually route decisions to new models, monitor metrics, and auto-rollback if performance degrades, with full traceability
Ready to Explore AgentOps?
See how ElixirData provides enterprise-grade agentops for mission-critical AI operations