AI-Driven Incident Response and Automated Remediation for Digital Media Platform

iheart · Media & Entertainment · 2025 · source Agentic: yes · promotional bias: med · source-backed · extracted with prompt 0.5.4 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Incident response for digital media infrastructure

iHeartMedia needed faster incident triage across monitoring dashboards, VPNs, cloud consoles, logs, and operational runbooks while serving always-on broadcast, streaming, and podcast traffic.

Constraints:

• 24/7 uptime expectations
• fragmented observability tools
• tribal knowledge dependency
• high on-call toil

Architecture

iHeart built a multi-agent SRE platform where Slack or PagerDuty events enter an orchestrator, context is assembled from operational data, specialized subagents perform analysis, and the answer returns as a Slack diagnosis with remediation options.

Diagram confidence: explicit (topology described by the source).

flowchart LR
  slack-and-pagerduty-triggers["Slack and PagerDuty triggers"]
  sre-orchestrator-agent["SRE orchestrator agent"]
  specialized-subagents["Specialized subagents"]
  operational-data-layer["Operational data layer"]
  bedrock-agentcore-and-strands-runtime["Bedrock AgentCore and Strands runtime"]
  recommended-actions["Recommended actions"]
  slack-response["Slack response"]
  slack-and-pagerduty-triggers --> sre-orchestrator-agent
  sre-orchestrator-agent --> specialized-subagents
  sre-orchestrator-agent --> operational-data-layer
  specialized-subagents --> operational-data-layer
  bedrock-agentcore-and-strands-runtime --> sre-orchestrator-agent
  bedrock-agentcore-and-strands-runtime --> specialized-subagents
  operational-data-layer --> recommended-actions
  recommended-actions --> slack-response

Components

Data flow

The incident flow begins with Slack or PagerDuty metadata, moves through an orchestrator that assembles context and delegates to specialized subagents, then returns root-cause and remediation recommendations.

Why this architecture

The architecture removes the manual login-and-dashboard hunt that consumed minutes before engineers could even begin diagnosing incidents.

Models used: AWS Bedrock models

Scale

• iHeart needed this for an always-on media platform where incident diagnosis is expected within 30 to 60 seconds.
  “understand what is the root cause and identify that and showcase what is the recommended options, right? All within 30 to 60 seconds.”transcript, stated
• The operational target is a 30- to 60-second root-cause and remediation recommendation loop.
  “understand what is the root cause and identify that and showcase what is the recommended options, right? All within 30 to 60 seconds.”transcript, stated
• Incident automation protects high-revenue live media operations where downtime can cost tens of thousands of dollars per minute.
  “if you think about potentially losing tens of thousand dollars every minute”transcript, stated
• The pre-AI response path consumed precious minutes across VPN, console, dashboard, and SSH login steps.
  “you need to log into AWS console, a number of monitoring dashboards and potentially SSH into production systems. Every such step, take precious minutes into that particular incident situation”transcript, stated

Alternatives considered (roads not taken)

One monolithic incident agent

Rejected because: The team chose specialized subagents coordinated by an orchestrator because incident tasks need different focused capabilities.

Adding more on-call people for repetitive triage

Rejected because: iHeart framed the goal as productivity amplification rather than staffing the same manual response loop.

Dashboard-first incident diagnosis

Rejected because: The Slackbot answer replaces engineers manually entering clusters and sifting logs for the first diagnosis.

Patterns

multi-agent-orchestration (operations)

A central SRE agent delegates incident questions to specialized subagents instead of relying on one monolithic incident agent.

observability-tracing (operations)

The agent workflow reads incident metadata and monitoring context, then summarizes the issue in Slack for faster triage.

Tradeoffs

How to structure incident intelligence.

Chose specialized subagents coordinated by an SRE orchestrator over one monolithic AI agent for every operational task.

Rationale: Specialization lets each agent focus on a type of incident activity while the orchestrator keeps the workflow coherent.

Gotchas (what bit them)

Manual incident response burned time before diagnosis even began.

Root cause: Engineers had to traverse VPNs, AWS consoles, dashboards, production systems, logs, and tribal knowledge during urgent outages.

Resolution: Route alert context into an orchestrated agent workflow that gathers evidence and presents the likely issue directly in Slack.

Evaluation

The presentation evaluates the agent by triage-response time and operational toil reduction in incident workflows.

Metrics: 30 to 60 second triage · reduced triage response time · operational efficiency

Outcomes

• The agent reduces incident triage work by surfacing likely issues in Slack instead of requiring manual log and dashboard navigation.
• The workflow preserves operational knowledge in reusable agent behavior.

Conference presentation with iHeartMedia and AWS; production metrics are self-reported.