Agentic Observability: How New Relic’s Agentic Platform Is Transforming AI-Driven Operations

At the developer event New Relic Advance, New Relic introduced its Agentic Platform, a no-code environment for building and governing AI agents directly within observability systems.

When Systems Break at 2 A.M., Who Fixes Them?

Imagine this scenario.

A payment gateway fails at 2 a.m. Traffic spikes. Customers abandon carts. Support queues explode.

The SRE team scrambles through dashboards, logs, and alerts. Slack channels flood with guesses. Someone restarts a service. Another scans telemetry data. Thirty minutes later, the problem surfaces.

The damage is already done.

Lost revenue. Frustrated customers. A stressed engineering team.

This reactive firefighting still defines operations across many enterprises. Engineers spend nearly 33% of their time responding to incidents instead of building better products.

But a new shift is emerging. AI agents are starting to move operations from monitoring problems to autonomously resolving them.

That shift took a major step forward at New Relic’s developer event New Relic Advance, where the company unveiled its Agentic Platform, a no-code environment designed to help enterprises build and manage AI agents directly within their observability stack.

The announcement signals a broader transformation: observability is evolving from insight to action.

What Is an Agentic Platform and Why CX Leaders Should Care?

An agentic platform allows organizations to create AI agents that analyze data, reason through problems, and execute actions autonomously.

Instead of humans responding to alerts, agents investigate incidents and trigger fixes automatically.

For CX and EX leaders, this matters more than it seems.

Every digital experience depends on system reliability. Slow apps, broken checkout flows, or unavailable services quickly destroy customer trust.

Observability tools traditionally help teams see problems faster.

Agentic AI helps them solve problems automatically.

According to International Data Corporation, enterprises increasingly see AI agents as the next evolution of IT operations.

As Stephen Elliot, Group Vice President at IDC, explains:

He adds that organizations deploying agents within strong governance frameworks will unlock a new level of operational efficiency.

Why Traditional Automation Is No Longer Enough

Rule-based automation struggles to handle modern cloud complexity.

Today’s digital environments contain microservices, APIs, containers, and distributed architectures. A single outage may involve dozens of interconnected services.

Traditional automation follows fixed rules:

• If server CPU spikes → restart instance
• If error rate rises → send alert

But real incidents rarely follow predictable patterns.

That creates three major operational problems.

1. Reactive Operations

Teams wait for alerts before investigating.

2. Alert Fatigue

Too many signals dilute focus.

3. Knowledge Silos

Institutional knowledge lives in engineers’ heads.

The result? Slower incident resolution and higher operational stress.

Agentic platforms aim to solve this by capturing expertise and embedding it into AI-driven workflows.

How Does the New Relic Agentic Platform Work?

The platform enables enterprises to build, deploy, and govern AI agents that analyze observability data and execute operational workflows.

Unlike standalone AI copilots, the platform integrates directly into observability pipelines.

This allows agents to operate where operational data already lives.

Key capabilities include:

No-Code Agent Builder

SREs and operations leaders can design agents using a drag-and-drop visual interface.

No coding skills required.

This democratizes AI across operations teams.

Pre-Built Expert Agents

The platform includes ready-to-use agents such as SRE Nerd, designed to accelerate adoption.

These agents handle common operational workflows immediately.

Dynamic Agent Runtime

Agents use reasoning logic to handle complex, multi-step investigations.

They adapt to unfamiliar failure scenarios.

Unified AI Orchestration

A centralized command center coordinates and manages agents across environments.

This allows organizations to scale automation safely.

What Makes Governance Critical in Agentic AI?

Autonomous agents require strict governance frameworks to ensure reliability and compliance.

Uncontrolled automation can create new operational risks.

The platform addresses this challenge through:

• Role-Based Access Control (RBAC)
• Audit logging for all agent actions
• Evaluation engines that test agent performance continuously

It also supports the Model Context Protocol (MCP) for secure tool access.

This governance layer builds trust in autonomous operations.

As Brian Emerson, Chief Product Officer at New Relic, explains:

In short, the platform attempts to bridge the talent gap and trust gap slowing AI adoption.

How Agentic Observability Changes Customer Experience

Operational resilience directly shapes digital customer experience.

Every CX leader knows the ripple effect of outages:

• Checkout failures hurt revenue
• App crashes increase churn
• Performance issues damage brand trust

Agentic observability introduces three powerful CX benefits.

1. Faster Incident Resolution

AI agents detect patterns across telemetry data instantly.

They investigate root causes faster than manual teams.

2. Proactive Problem Prevention

Agents analyze signals continuously.

They flag risks before customers notice issues.

3. Reduced Operational Burnout

SREs spend less time firefighting.

They focus on innovation and reliability improvements.

These improvements translate into better uptime, smoother journeys, and happier customers.

Which Enterprises Are Already Using New Relic?

Organizations worldwide rely on New Relic’s observability platform to maintain digital experiences.

These include companies such as:
Adidas Runtastic
Domino’s
Swiggy
Ryanair
Topgolf
William Hill

These businesses operate complex digital environments where downtime directly impacts revenue.

Agentic capabilities could significantly accelerate operational automation across such ecosystems.

What Implementation Challenges Should CX Leaders Expect?

Agentic automation introduces cultural and technical challenges.

Organizations must prepare for several hurdles.

Skill Gaps

Many teams lack experience building AI workflows.

Governance Complexity

Autonomous systems must align with security policies.

Change Management

Teams must trust agents to take operational actions.

Key Insights for CX and DevOps Leaders

Agentic AI represents a shift from observability insights to automated action.

Here are the most important implications:

• Observability evolves into operational automation.
• AI agents reduce engineering firefighting.
• No-code builders expand AI adoption beyond data scientists.
• Governance frameworks become critical for enterprise trust.

Organizations that integrate these capabilities early could gain a strong operational advantage.

Common Pitfalls to Avoid

Before deploying agentic AI, enterprises should watch for common mistakes.

Over-automation too quickly
Start with narrow workflows before scaling.

Ignoring governance frameworks
Autonomous systems require strict controls.

Underestimating cultural resistance
Teams must trust AI before relying on it.

Fragmented observability data
Agents need unified telemetry for accurate decisions.

FAQ: Agentic AI and Observability

What is agentic AI in observability?

Agentic AI refers to autonomous systems that analyze telemetry data, investigate incidents, and execute remediation actions without human intervention.

Why are enterprises investing in AI agents?

AI agents reduce operational toil, accelerate incident resolution, and enable teams to focus on innovation rather than firefighting.

How does no-code automation help operations teams?

No-code tools allow domain experts like SREs to build automation workflows without programming expertise, accelerating adoption.

What governance is required for AI agents?

Enterprises need RBAC controls, audit logging, testing frameworks, and security protocols to ensure agents act safely.

How will AI agents affect DevOps roles?

AI agents will handle repetitive tasks, allowing engineers to focus on architecture, reliability engineering, and innovation.

When will agentic observability become mainstream?

Industry forecasts suggest 40% of enterprise applications will include AI agents by 2026, indicating rapid adoption.

Actionable Takeaways for CX and Operations Leaders

1. Audit your observability maturity. Ensure telemetry data is unified before deploying AI agents.
2. Start with high-impact workflows. Automate repetitive incident investigations first.
3. Capture institutional knowledge. Convert SRE expertise into agent workflows.
4. Implement governance early. Use RBAC, audits, and evaluation frameworks.
5. Build cross-functional teams. Align DevOps, CX, and security leaders.
6. Measure operational impact. Track incident resolution times and downtime reductions.
7. Scale gradually. Expand automation once agents prove reliable.
8. Focus on customer outcomes. Faster resolution means better digital experiences.

The rise of agentic AI marks a turning point in enterprise operations.

Observability platforms no longer just detect problems.

They increasingly solve them automatically.

For CX leaders navigating complex digital ecosystems, this shift could redefine reliability, resilience, and the customer experience itself.

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