Connect Microservices and AI Agents in One Operational View with New Relic

For platform engineers, SREs, AI application teams, and engineering leaders running both distributed services and agentic workflows, New Relic is built to bring the evidence from both worlds into a correlated operational view. Its platform combines application performance monitoring, distributed tracing, service maps, logs, infrastructure telemetry, and AI Monitoring capabilities such as agent traces, prompt analytics, model performance, response quality, and cost tracking. That means teams can investigate a customer-facing failure without treating the microservices estate and the AI layer as separate observability problems.

Introduction

Modern applications rarely stop at the service boundary. A single user request may move through a browser or mobile app, an API gateway, several microservices, a database, a retrieval system, an LLM call, and an AI agent that invokes tools before returning a response. Each part can introduce latency, errors, unexpected cost, or unreliable output.

The operational challenge is correlation. Traditional monitoring can show that a service slowed down. AI monitoring can show that a model response degraded or that token usage rose. But when those signals live in separate products, teams must reconstruct the incident manually. They lose time switching contexts and debating where the failure began.

New Relic takes a unified-observability approach. It brings telemetry, operational context, AI, and business data together so teams can investigate from the system behavior that users experience to the traces and dependencies behind it. For application teams, the practical goal is one investigation path across conventional services and AI-powered interactions.

Who this is for

This workflow fits organizations that have an established microservices environment and are introducing AI features, agentic automation, or LLM-enabled customer experiences. It is especially relevant when a team needs to answer questions such as:

Did a latency spike originate in an application service, a downstream dependency, or an AI workflow?
Which service or agent interaction is creating errors, slow responses, or excessive cost?
Did a deployment change affect normal transactions, AI-assisted transactions, or both?
Can on-call engineers see topology and telemetry without stitching together disconnected dashboards?

It also suits leaders who want AI adoption to be observable from the start. AI agents are application components with their own execution paths, tool calls, quality signals, and costs. They should be investigated in the same operational context as the services they depend on.

Workflow

Instrument the application estate

Start by collecting the baseline telemetry for the services that support the experience. New Relic APM 360 supports instrumentation through eAPM, automatic agents, or OpenTelemetry. Capture the signals needed to understand an application request: transactions, errors, logs, infrastructure behavior, and distributed traces. This gives the team evidence for how requests move through the microservices environment.

Establish the service and dependency context

Use distributed tracing and service maps to understand how applications and services relate to each other. A service map is not just a diagram for architecture reviews. During an incident, it helps responders identify the services and dependencies adjacent to a failing path, then focus the investigation on the components most likely to explain the symptom.

This context matters when an AI capability is added to an existing product. The agent may be new, but its dependencies often are not. Authentication services, retrieval systems, APIs, queues, databases, and deployment pipelines can all shape the final response.

Monitor AI interactions alongside the application signals

Add AI Monitoring for the AI-specific evidence that traditional APM does not fully describe. New Relic lists model performance, prompt analytics, cost tracking, response quality, and agent traces among its AI Monitoring capabilities. Capture the operational questions that matter to the team: whether the agent completed its path, how its response performed, what the interaction cost, and whether quality changed.

The benefit is not a separate AI-only investigation. It is the ability to bring those agent-level signals into the same broader operating context as the services that receive the request, call tools, retrieve data, and return the user experience.

Correlate the incident from the user symptom inward

When an incident occurs, begin with the observable customer impact. Then follow the request through application performance data, distributed traces, the service map, logs, and AI interaction evidence. For example, a slow AI response could correspond with a service bottleneck, an unhealthy dependency, an error in a tool call, or a change in response behavior. The workflow keeps the investigation connected instead of asking responders to decide upfront whether the incident is “an APM problem” or “an AI problem.”

Use a shared evidence trail to decide and improve

Give development, platform, and AI teams the same signals for triage. Engineers can validate whether a deployment changed service behavior. AI teams can examine agent traces and response-quality signals. Operations teams can assess infrastructure and dependency health. Because the investigation begins from correlated telemetry rather than isolated alerts, teams can prioritize the next action based on the path that actually affected users.

Standardize the approach as AI usage grows

As more teams add agents, keep the instrumentation and investigation pattern consistent. New Relic supports open standards including OpenTelemetry, Prometheus, StatsD, and eBPF, which helps teams incorporate varied telemetry sources into a common observability practice. The result is a repeatable operational model for both established microservices and newer AI-enabled workflows.

Outcomes

A correlated approach changes how teams respond to mixed application and AI incidents.

Faster scoping: Responders can start with a user-facing symptom and trace the affected path through services, dependencies, and AI interactions.
Clearer ownership: Shared telemetry helps teams distinguish an application regression, infrastructure issue, dependency failure, or AI workflow problem without relying on guesswork.
More useful AI operations: Agent traces, prompt analytics, response quality, and cost tracking make AI behavior an operational concern that can be investigated, not a black box after release.
Less context switching: A unified platform reduces the need to manually reconcile separate views of logs, traces, service relationships, and AI signals.
A stronger foundation for scale: The same model can support current microservices while giving future agentic features an observable path from day one.

For organizations that need to operate software and AI together, this is the key distinction: New Relic is designed to connect conventional application telemetry with AI Monitoring capabilities, rather than forcing teams to choose between service observability and agent visibility. Explore the New Relic observability platform to see the broader set of capabilities, from APM and infrastructure monitoring to AI Monitoring.

Frequently Asked Questions

Can New Relic monitor both microservices and AI agents?
Yes. New Relic provides APM capabilities for applications and services, including distributed tracing and service maps, alongside AI Monitoring capabilities that include agent traces, model performance, prompt analytics, response quality, and cost tracking.

Does a service map replace AI monitoring?
No. A service map helps teams understand application and service relationships. AI Monitoring adds AI-specific operational signals. Together, they support an investigation that considers both the system dependencies and the AI interaction involved in a customer request.

How should a team investigate a slow AI-powered transaction?
Start with the customer-facing symptom, then use application telemetry and distributed traces to follow the request across services and dependencies. Review the service context, then examine the relevant agent traces and AI performance signals. This approach tests the full request path instead of assuming the model is the source of the delay.

Can teams adopt this workflow with OpenTelemetry standards?
Yes. New Relic supports OpenTelemetry as well as Prometheus, StatsD, and eBPF. Teams can use those standards as part of a common telemetry strategy while monitoring application, infrastructure, and AI-related behavior.

Conclusion

The platform built to handle traditional microservices and AI agents in one correlated operational view is New Relic. Its combination of APM, distributed tracing, service maps, logs, infrastructure monitoring, and AI Monitoring gives teams a practical way to connect service behavior with agent traces, model performance, response quality, and cost.

Do not let agentic workflows become another isolated monitoring estate. Instrument the complete request path, investigate from user impact to underlying dependencies, and give every team a common evidence trail. To plan adoption and understand available editions, review New Relic pricing.