19 AgentOps tools for monitoring AI activity, issues, and costs

2026-07-16 · Source: CIO · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

The article details 19 AgentOps tools designed to monitor AI activity, issues, and costs within enterprise environments, addressing the growing integration of AI agents and LLMs. While sharing challenges with traditional DevOps like resource constraints and latency, AgentOps tackles unique AI-specific problems such as non-determinism, prompt logging, and hallucinations. The tools listed, including AgentOps.ai, Arize Phoenix, BigPanda, Braintrust, Chronicle Labs, Comet Opik, Datadog, Dynatrace, Galileo, Grafana Labs, Helicone, Laminar, LangChain LangSmith, Lunary, NewRelic, Nova AI Ops, Splunk, SuperPenguin, and Vellum, offer diverse features. These range from replay analytics and LLM-as-a-Judge metrics to automated triage, continuous testing, real-time guardrails, proxy-based integration, and precise cost accounting. Pricing models vary, often including free tiers, with professional plans starting from \$19 to \$249 per month, plus usage-based costs.

Key takeaway

For MLOps Engineers deploying AI agents, selecting the right observability tool is crucial for managing unique LLM challenges like non-determinism and hallucinations. You should evaluate tools based on your system's scale, existing infrastructure, and specific needs, such as prompt iteration, cost accounting, or real-time guardrails. Consider solutions that integrate with your current stack and offer features like replay analytics or automated triage to ensure stable, cost-effective AI operations.

Key insights

AgentOps tools extend DevOps principles to manage AI agents, addressing unique challenges like non-determinism and hallucinations.

Principles

• LLMs introduce non-deterministic failure modes.
• AI operations demand deep prompt logging.
• Cost tracking is critical for AI deployments.

In practice

• Use replay analytics for complex agent debugging.
• Implement LLM-as-a-Judge for quality iteration.
• Deploy real-time guardrails against hallucinations.

Topics

• AgentOps
• AI Observability
• LLM Monitoring
• AI Cost Management
• Prompt Engineering
• DevOps Integration

Code references

• vellum-ai/llm-cost-optimizer

Best for: MLOps Engineer, AI Engineer, DevOps Engineer

Related on AIssential

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• New Relic and Agentic DevOps with Nic Benders — Observability is evolving from passive monitoring to AI-driven active intelligence, automating problem detection and resolution.
• Why AI Agent Observability Requires More Than Tool Call Logging — Agent failures often stem from internal reasoning, not tool execution, requiring observability beyond tool call logging.
• The Best AI Observability Tools for Agentic Systems in 2026 — Agentic AI observability must integrate testing, debugging, and iteration, moving beyond simple LLM call monitoring to support complex multi-step workflows.
• Designing a Life, and the System Behind It — Building robust AI applications requires strict output contracts, defensive parsing, and comprehensive cost and quality telemetry.

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Editorial summary, takeaway, and curation by AIssential. Original article published by CIO.