Beyond the Black Box: Next-Gen AI Agent Monitoring

The promise of AI has shifted. We have moved rapidly from simple "chat" interfaces to sophisticated agentic AI systems, developing architectures where autonomous agents don’t just process text, but make decisions, invoke tools, and collaborate with other agents to execute complex business workflows.

But for the engineers tasked with keeping these systems alive, this shift has introduced a massive transparency gap. Traditional APM tools were built for predictable, linear service calls. Agentic environments, however, are dynamic, probabilistic, and non-linear. When an agentic workflow fails, it’s rarely a simple error condition. Instead, it’s a planning agent failing to hand off a task, a coding agent misinterpreting a tool’s output, or a loop of inefficient agent-to-agent communication that burns through your API budget without delivering a useful result.

Today, at New Relic Advance February 2026, we are closing that transparency gap. We are excited to announce the expanded functionality of our existing AI Monitoring offering with AI Agent Monitoring, featuring a suite of capabilities now in preview, designed to visualize, debug, and optimize the complex orchestration of multi-agent systems.

The Visibility Gap: Why Traditional Monitoring Fails Agents

In an agentic ecosystem, the workload is no longer just a collection of servers, it is a dynamic decision graph. Consider a typical software assistant: a Planning Agent interprets a user request, delegates a task to a Research Agent, which calls an External Search Tool, and then passes the data back to a PR Agent. If the final response is poor, where did the failure occur? Was it the prompt logic in the first agent? An error in the third-party tool? Or a latency spike in the hand-off between agents?

Existing tools make it incredibly difficult to see these interactions. Engineering teams are currently facing three critical pain points:

1. Unseen Interactions: Traces often show LLM calls but fail to show which specific agent made the call and which tool it utilized.
2. Fragmented Context: There is no Service Map for agents. Teams can’t see the link between Agent A and Agent B, leading to blind spots in dependency mapping.
3. Slow Root Cause Analysis (RCA): SREs and AI engineers spend hours manually scavenging through logs to reconstruct the chain of events, leading to extended MTTR and lost revenue.

New Relic AI Agent Monitoring changes this by organizing AI observability within the appropriate APM context, providing a full-stack view from the infrastructure layer to the agent's decision logic. This means your AI agents are fully integrated within New Relic’s comprehensive, end-to-end observability ecosystem. By nesting AI telemetry directly inside the APM experience, you can, for example, trace a single user request from the frontend browser, through the underlying cloud infrastructure and database calls, all the way into the complex reasoning loops of your AI agents.

This advanced visibility is a native extension of our core APM platform, so customers can leverage this out-of-the-box solution immediately using our existing APM agents, unlocking the ability to correlate AI performance with system health to protect your engineering investments and your bottom line without any incremental spend.

New for February 2026: The Strategic Command Center for AI

Our latest release introduces three game changing capabilities that transform AI monitoring from basic logging into a strategic command center.

Agents Service Map to Visualize the Ecosystem

For the first time, you can see your entire multi-agent collaboration in one view. The Agents Service Map provides a real-time visualization of all interactions. Whenever Agent A calls Agent B, a link is drawn.

This eliminates dependency blind spots instantly. You can see how a failure in a minor utility agent bubbles up to impact your primary user-facing agent. By seeing the inter-connected nature of your system, you can optimize the orchestration to reduce redundant calls and improve the overall information flow.

Granular Behavioral Insights

Observability is about more than just knowing that a service is "up or down." In the world of AI, behavior is performance. By clicking on any agent within the Service Map, engineers gain access to a dedicated Agent Performance dashboard.

• Request Volume: Are your agents looping or making excessive calls?
• Average Latency: Where is the friction in the reasoning chain?
• Error Percentages: Is a specific tool call consistently failing for a specific agent?

This data allows AI Product Managers to quantify the ROI of their agents and allows SREs to ensure the underlying infrastructure is scaling appropriately as agent usage grows.

Agent Drill Down

When an anomaly is detected, speed is everything. Our new Agent Drill Down capability allows you to pivot from the high-level Service Map directly into the specific trace of the agent or tool in question.

No more searching for GUIDs. You can see the exact order of calls, the inputs/outputs of the agent, and the specific metadata associated with the failure. This level of clarity allows teams to distinguish between a technical timeout and a logic failure, slashing MTTR and protecting the bottom line.

Unified Orchestration

While some of our competitors focus on monitoring individual LLM models and providing individual agent-step evaluations, New Relic offers Unified Orchestration Visibility. We treat the agentic system as a cohesive whole. Because our monitoring is built on the Entity Platform, your AI agents aren't just "objects" in a log, they are first-class citizens in your observability stack.

• No Manual Instrumentation: Using our extensive and growing protocol support (e.g., LangGraph, AutoGen, Strands), you get detailed visibility into agent-to-agent communication out of the box.
• Expanded Protocol Support: We continue to lead the market with support for the ConverseAPI (AWS Bedrock) and other emerging protocols, ensuring that your monitoring stack evolves as fast as the AI landscape.
• Distributed Tracing with Context: We've integrated AI Agent Spans into our waterfall views, so you can see AI calls right alongside your database queries and legacy API calls.

Aligning Engineering and Business

One of the greatest challenges in AI is that every stakeholder needs a different set of data. New Relic AI Agent Monitoring provides a single source of truth that serves the entire organization:

• For AI/ML Engineers: It’s a debugger. You can trace agent decisions, monitor tool usage, and fine-tune behavior based on live performance data.
• For SREs & DevOps: It’s a safety net. You can prevent infrastructure-related downtime, manage the monetary costs of AI workloads, and scale resources with precision.
• For Product Managers: It’s an ROI engine. You can measure the success rates of automated tasks and ensure your AI is actually delivering business value to the end user.
• For Security & Compliance: It’s an audit trail. You can monitor agent access to sensitive data and ensure that agents are operating within ethical and regulatory boundaries (like GDPR).

Observability Empowers Innovation

Unreliable AI systems don't just cause technical headaches—they erode customer trust and bleed revenue. Without the ability to monitor complex, dynamic, and probabilistic AI architectures, businesses face significant challenges in detecting biases, understanding limitations, and identifying bottlenecks.

New Relic AI Agent Monitoring transforms the "black box" of agentic AI into a transparent, manageable engine for growth and adding new capabilities to our existing AI Monitoring offering. By accelerating troubleshooting, eliminating context switching, and providing a comprehensive full-stack view, we enable you to put agentic workflows into production with confidence.

Stop chasing ghosts and start managing your AI with precision.

Ready to see your agents in action?

• AI Agent Monitoring is part of New Relic AI Monitoring, request a demo or start your free trial today.
• Sign up here to preview the features announced today.