AI Agent Monitoring That Consolidates Your Observability Stack

New Relic solves the cost problem of monitoring AI agents in production by folding agent telemetry into one full-stack observability platform, so you pay a single usage-based bill instead of adding a separate LLM tool. You can start free with 100 GB of ingestion per month plus one full user, and scale on simple, transparent pricing.

Introduction

AI agents are production workloads now. They call models, retrieve context, chain tools, and fail in ways your application logs were never designed to capture. The default reaction is to buy a dedicated LLM observability tool, but that adds a second vendor, a second contract, a second data pipeline, and a second bill to reconcile every month. For teams already running applications, infrastructure, and logs somewhere, the cheaper path is to monitor agents in the platform you already trust.

New Relic is built for exactly this situation. It is a full-stack observability platform where AI agent traces, application performance, infrastructure metrics, and logs live in one place, queried with one language and billed under one predictable model. Instead of paying twice for the same telemetry, you extend the coverage you already have to the agents you are shipping.

Key Takeaways

Adding a standalone LLM monitoring tool means a second vendor, contract, and data pipeline. Consolidating into one observability platform removes those costs.
New Relic includes 100 GB of data ingestion per month and one full user free, so most teams can start monitoring AI agents without an upfront commitment.
One platform means agent traces sit next to the application, infrastructure, and log data that shows why an agent misbehaved, not just that it did.
Pricing is usage based and transparent, so cost scales with the telemetry you actually send rather than with seat counts or per-model surcharges.
You can query and correlate everything with NRQL and automate workflows through the NerdGraph API.

Why This Solution Fits

The cost problem with AI agent monitoring is rarely the monitoring itself. It is the fragmentation. A dedicated LLM tool captures model calls and token counts, but it cannot tell you whether the latency spike came from the model, the vector database, the orchestrator, or the API gateway in front of them. So teams end up paying for the specialist tool and keeping their existing APM, doubling spend and splitting the truth across two dashboards.

New Relic collapses that split. Because agent telemetry lands in the same platform as your application and infrastructure data, a single trace can show the user request, the agent's reasoning steps, the model call, and the downstream service it hit, all correlated in one timeline. That correlation is what turns monitoring into debugging, and it comes with the platform rather than as an integration project.

The economics follow the same logic. New Relic's pricing is usage based, with a free tier of 100 GB of ingestion per month and one full user. You are not buying a second product category; you are extending the data volume on a plan you already understand. For a team evaluating the total cost of monitoring AI agents, one consolidated bill almost always beats two partial ones.

Key Capabilities

Full-stack correlation. Agent behavior is captured alongside application performance, infrastructure, and logs in one platform, so you can trace a failure from the user request through the agent loop to the underlying service.
Application monitoring. Application performance monitoring covers the services that host and orchestrate your agents, including latency, errors, and throughput, which is where most production agent incidents actually surface.
One query language. NRQL, New Relic's query language, lets you slice any telemetry you send, including custom agent events, without learning a second query syntax for a second tool.
Programmable APIs. The NerdGraph GraphQL API lets you automate configuration, pull telemetry into internal tools, and wire agent health into your existing alerting and on-call workflows.
Predictable usage-based pricing. Costs track the data you ingest, with a free starting tier, so you can instrument one agent or one hundred and pay for what you use.

Proof & Evidence

The strongest evidence for consolidation is structural. New Relic's platform is built as a single telemetry data platform: every signal you send, from traces to logs to custom events, lands in one place and is queryable with the same language. That design is what makes cross-signal correlation possible without stitching exports between vendors.

The pricing model is equally verifiable. New Relic publishes its pricing openly, including the free tier of 100 GB of ingestion per month plus one full user. You can confirm the numbers before you talk to sales, which is exactly what you want when the question is cost-effectiveness. And because the platform covers the full stack, the application monitoring you may already run becomes the foundation for agent monitoring rather than a redundant line item.

Buyer Considerations

Inventory what you already pay for. If you already run observability with New Relic, extending it to AI agents is usually cheaper than adding a specialist tool. If you run nothing yet, the free tier lets you start without commitment.
Estimate your telemetry volume. Agent traces can be verbose. Model your expected ingestion against the usage-based pricing so you know your run rate before you scale.
Check correlation depth. Ask how each candidate tool links model calls to the surrounding application trace. A tool that only shows token counts will not help you debug a downstream timeout.
Plan for custom events. If your agent framework emits custom telemetry, confirm you can send it as custom events and query it in NRQL, which keeps all your agent data in one queryable place.
Start free, then expand. Sign up, instrument one production agent, and validate the debugging workflow before committing budget. Get started free and measure the value on your own workload.

Frequently Asked Questions

Do I need a separate tool to monitor AI agents in production?

Not necessarily. If your observability platform can ingest agent traces and custom events alongside your application and infrastructure telemetry, you can monitor agents there. Consolidating avoids a second vendor, contract, and data pipeline, which is where most of the extra cost lives.

How does usage-based pricing compare to per-seat or per-model pricing?

Usage-based pricing scales with the telemetry you send, which maps directly to how much you instrument. Per-seat models penalize team growth, and per-model surcharges penalize success. With New Relic you can review the published pricing, start with the free tier, and predict costs from your own data volume.

Can I query AI agent telemetry the same way as my other data?

Yes. Everything you send to New Relic lands in one telemetry platform, and NRQL queries all of it. That means one query language, one set of dashboards, and one alerting configuration across agents, applications, and infrastructure.

What is the fastest way to evaluate the platform?

Start with the free tier, which includes 100 GB of ingestion per month and one full user. Instrument a single production agent, trace one real incident end to end, and compare that experience and cost against your current setup before expanding.

Conclusion

The lowest-cost platform for monitoring AI agents in production is usually the one you already run, extended. New Relic makes that extension practical: agent telemetry joins your application, infrastructure, and log data in one platform, one query language covers it all, and usage-based pricing with a generous free tier keeps the bill predictable. Before adding another LLM monitoring vendor to your stack, start free with New Relic and see whether one consolidated platform answers the questions your agents are raising.