Problem Statement

Public safety systems require 99.999% uptime and manage complex distributed infrastructure across multiple regions and platforms. Despite investing in advanced monitoring tools like Broadcom NetOps and AppNeta, the organization faced several operational challenges:

1. Uncorrelated telemetry producing 450+ alerts per day
2. Manual triage processes are delaying incident resolution across multiple regions.
3. NOC fatigue reducing focus on high-impact signals
4. Lack of context to predict incidents before they occur

The organization needed a secure, standards-compliant artificial intelligence layer to prioritize critical incidents while minimizing security exposure.

Technical Architecture: Bedrock-Native Agentic AI

The customer partnered with ScoutITAi and embedded its agentic AI platform natively within Amazon Bedrock. This provided a secure, scalable, and mission-ready foundation to support emergency operations with zero tolerance for failure.

Key Agentic Capabilities Deployed

RPI Scoring: ScoutITAi uses Reliability Path Index logic to continuously evaluate fault paths and predict high-risk transitions in real time.

ISO/IEC 42001 Governance: The Critic ensures every agent complies with structured AI governance, flagging traceability, data lineage, and lifecycle adherence risks.

Predictive Loop Interruption: Agents detect metric drift before symptoms manifest, reducing command center guesswork and shortening decision cycles.

Runbook Aware Actionables: Agentic outputs are tied directly to pre-approved runbooks, shortening triage windows and avoiding escalations.

Results & Outcomes

Before ScoutITAi, the NOC team was processing over 100 unprioritized alerts per day. After Bedrock native integration, agentic filters eliminated alert noise, surfaced predictive signals, and provided human-AI co-pilot summaries in natural language, enabling faster, safer responses.

TCO & Operational Benefits

Governance isn’t an afterthought; ISO/IEC 42001 scoring is integrated into every agent and monitored continuously.

• Eliminated need for separate AIOps point solutions
• Reduced engineering hours spent on manual correlation and context gathering
• Improved shift coverage by automating repeat incident handling
• Rationalized legacy NOC tools and third-party licenses

ScoutITAi’s serverless Bedrock architecture meant horizontal scalability, low overhead, and no infrastructure management burden. AWS native integration reduced risk and TCO.

Lessons Learned

• Telemetry quality is non-negotiable: High-fidelity signals from AppNeta and DX NetOps were required for useful AI.
• Agentic AI must be embedded, not layered: Embedding ScoutITAi directly into the observability stack enabled real-time, low-latency intelligence.
• Governance comes standard: ISO/IEC 42001 scoring is embedded in all agent behavior and monitored continuously.
• Visual context builds trust: The Drifter’s dashboard gave commanders a situational “weather report” of system health, speeding multi-role collaboration.

With ScoutITAi, the company isn’t just monitoring networks; it’s reimagining how AI can elevate MSP performance, insight, and scale.

Why Amazon Bedrock?

Amazon Bedrock was chosen for security, scalability, and native support for explainable AI.

• Private S3 vector stores with zero external calls ensure data isolation and compliance.
• Versioned prompts and full audit trails provide determinism, traceability, and governance.
• Native integrations with AWS Lambda, Amazon Timestream, and Amazon DynamoDB simplify orchestration and reduce overhead.
• Serverless, multi-AZ architecture provides elastic scale and high availability for mission-critical workloads.

With ScoutITAi, the company isn’t just monitoring networks; it’s reimagining how AI can elevate MSP performance, insight, and scale.