The Problem: Alert Fatigue and Slow Triage

In January 2025, a mid-stage SaaS platform handling 10 million API requests per day faced a crisis. Their on-call engineer was drowning in alerts. Each incident required manual context switching across New Relic dashboards, log aggregators, and Jira. Average time to acknowledge an alert was 12 minutes. Time to identify root cause was 45 minutes. The team of five engineers was spending 35% of their on-call hours on low-value triage loops instead of building.

The core issue was not lack of monitoring – it was the absence of an intelligent triage layer. Alerts triggered PagerDuty notifications, but the receiving engineer had to manually query New Relic for relevant metrics, check recent deployments from GitHub, and parse log streams to form a hypothesis. By the time they understood the problem, the incident had often escalated.

The Solution: An Agentic Incident Triage Assistant

The team decided to build an agent that could autonomously execute the first 15 minutes of triage. They needed a system that when an alert fired, would gather context, analyze recent changes, produce a root cause hypothesis, and create a tracked task – all without human intervention.

They evaluated low-code platforms and settled on n8n (self-hosted v1.78), citing its flexibility with custom JavaScript nodes and native support for GraphQL and REST APIs. The core agent would use a decision-tree approach with conditional branches rather than LLM-based reasoning for deterministic reliability, though they reserved an OpenAI node for natural language summary generation.

Architecture Overview

[PagerDuty Alert] -> (Webhook Trigger) -> n8n Workflow
  |
  ├─ Step 1: Extract alert payload (severity, service, timestamp)
  ├─ Step 2: Query New Relic MCP Server for:
  │    - Top 5 error traces in the last 15 minutes
  │    - APM throughput and error rate anomalies
  │    - Infrastructure metrics (CPU, memory, disk)
  ├─ Step 3: Fetch recent deployments (GitHub releases in last hour)
  ├─ Step 4: Generate root cause summary (via OpenAI GPT-4o-mini)
  ├─ Step 5: Create Asana task with:
  │    - Summary, severity, timeline
  │    - Evidence links (direct to New Relic query results)
  │    - Suggested next-step action
  └─ Step 6: Post Slack thread with triage output

Key design decisions:

• Idempotency key: Each alert ID is hashed to prevent duplicate task creation.
• Retry backoff: If New Relic returns incomplete data, the workflow retries up to three times with exponential backoff (1s, 4s, 9s).
• Fallback: If the MCP endpoint fails, the agent falls back to raw New Relic REST API (NerdGraph) queries using a custom HTTP node.

Implementation Details

The n8n workflow uses the following nodes and configurations:

Trigger: PagerDuty Webhook

• Receives incident.trigger events.
• Extracts incident.id, service.summary, severity from payload.

New Relic MCP Call

• GraphQL query to fetch error group samples:

{
  actor {
    nrql(query: "SELECT count(*) FROM TransactionError WHERE appName = '{{$json.service}}' SINCE 15 minutes ago FACET error.class" ) {
      results
    }
  }
}

• The MCP server returns structured JSON with error classes, timestamps, and occurrence counts.

GitHub Deployments

• Uses GitHub REST API GET /repos/owner/repo/deployments?environment=production&per_page=5.
• Filters for deployments within the last 60 minutes.

OpenAII Node (Summary Generator)

• Prompt template:

Given the following incident context, produce a concise root cause hypothesis (1-2 sentences) and list three evidence points.
Incident: {{$json.alert_description}}
Error traces: {{$json.error_traces}}
Recent deployments: {{$json.recent_deployments}}

• Temperature set to 0.2 for deterministic output.

Asana Create Task

• Custom fields: severity, MTTF (minutes to triage), source.
• Assignee is automatically set to the on-call engineer via a lookup table (stored in n8n credentials).

Results and Metrics

After four weeks in production, the team observed:

The 85% coverage means that for four out of five alerts, the agent produced a usable root cause summary that the engineer could verify and act on within minutes. The remaining 15% were alerts with insufficient telemetry data (e.g., custom metrics not yet instrumented).

One nuance worth noting: The team initially attempted an LLM-first approach where the agent would autonomously decide which data sources to query. They found this introduced unpredictability. The deterministic decision tree (alert->fetch metrics->fetch deploys->summarize) proved both faster and more reliable.

How Neura Market Templates Accelerate This

Building such a workflow from scratch took the team roughly 40 hours over two sprints. Neura Market hosts a pre-built Agentic Incident Triage template that replicates this exact architecture – n8n workflow, New Relic MCP config, Asana schema, and PagerDuty webhook receiver. The template:

• Ships with documented environment variables for API keys.
• Includes retry logic and error handling nodes.
• Provides a customizable Asana task template with the fields described above.

Since publishing the template, over 120 teams have forked it. One e-commerce company reported reducing their weekly incident response overhead from 20 hours to 6 hours.

Building on This Pattern

The incident triage agent is a specific implementation of a broader pattern: agentic data-to-action pipelines. The same architecture can be repurposed for customer support ticket triage (Zendesk + Datadog) or security incident response (Cloudflare + Splunk). Neura Market's directory includes templates for those variants, using Zapier, Make.com, and Pipedream alongside n8n.

For teams considering this approach, three lessons stand out:

1. Start with a narrow scope – automate the first 15 minutes of triage, not the entire incident lifecycle.
2. Use deterministic logic for data gathering before applying LLM summarization. The agent should fetch, then synthesize, not hallucinate.
3. Instrument the agent itself: log every run to a dedicated New Relic dashboard so you can measure false positives and missed incidents.

The result is not just faster triage. It is a documented, reproducible process that junior engineers can understand and that the team can iterate on. That is the value of agentic automation when built with discipline.

Frequently Asked Questions

What is the best way to get started with Agentic Incident Triage: How We Automate?

The best approach is to start with a clear goal in mind. Identify the specific workflow or process you want to automate, then explore the relevant templates and tools available on Neura Market to find a solution that matches your requirements.

How much does workflow automation typically cost?

Costs vary significantly depending on the platform and scale. Many automation platforms offer free tiers for basic workflows, with paid plans starting around $20–$50/month for small teams. Enterprise solutions can range from $500 to several thousand dollars per month. Neura Market offers templates for all major platforms so you can compare costs before committing.

Do I need technical skills to implement workflow automation?

Modern no-code and low-code platforms like Zapier, Make.com, and others have made automation accessible to non-technical users. Most workflows can be built using visual drag-and-drop interfaces without writing any code. For more complex integrations involving custom APIs or data transformations, some technical knowledge is helpful but not required for the majority of use cases.