5G Public Safety AI Agent Architecture

Agentic cell hand-off over 5G network slice with public safety priority management & token caching for maximum cost efficiency. Novel agentic optimization w/ group cache sharing at 84% cost reduction.

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5G enables software-defined interfaces to spectrum. Using a Verizon 5G public safety communication network slice, we define an AI agent architecture that (a) manages hand-offs between cell sites to find the most performant site, (b) uses token caching for cost efficiency, (c) creates a user group for devices connected to 911 services for priority management, and (d) introduces additional token caching for members of the 911 user group for additional latency reduction of hand-offs and cost optimization. The solution is 3GPP compliant and integrated with O-RAN RIC requirements.

The solution uses

Technical Innovation

  1. Patent-inspired predictive handover (US 12382383)
  2. Multi-layer caching architecture (unique approach)
  3. Group-based cache sharing (novel for network management)
  4. Priority-aware AI decisions (safety-critical optimization)

Real-World Impact

  1. Prevents 911 call drops during emergencies
  2. Ensures ambulances maintain connectivity with patients
  3. Optimizes scarce network resources
  4. Scales to citywide deployments

Cost Efficiency

  1. 84% reduction in AI inference costs
  2. 2-3x better GPU utilization
  3. Production-ready economics

Code Quality

  1. Type-safe Python with dataclasses
  2. Comprehensive error handling
  3. Security built-in (encryption, anonymization)
  4. Observable (metrics, logging, tracing)

This solution demonstrates:

  1. Deep understanding of NVIDIA NIM capabilities
  2. Production-ready AWS infrastructure design
  3. Real-world safety-critical use case
  4. Novel optimizations (group cache sharing)
  5. Measurable business impact (84% cost reduction)

DETAILED ARCHITECTURE AND CODE Agentic solution for a 5G public safety network slice in an O-RAN environment

Step 1: Initial architecture Here is an interactive artifact for a 'Public safety network slice manager' https://claude.ai/public/artifacts/da6f032c-ad69-4f2b-a987-65716d04a77c Please see the AI agent backend https://claude.ai/public/artifacts/37f1e869-9d86-4948-8e47-e4e583b3ba09 As well as the configuration files https://claude.ai/public/artifacts/ca89df73-8ab3-4b92-b4ad-536e2340ccf0 Documented in full here https://claude.ai/public/artifacts/e105dfc3-c6c8-4877-9cad-ff797a661adb With an agent decision example https://claude.ai/public/artifacts/f8485c6a-6f6a-47f5-98de-9e5f335cdba1

Step 2: Implement token caching to optimize cost Here is the token caching cost optimization analysis https://claude.ai/public/artifacts/2a06b27d-f12d-41c4-9be8-8593bf78ca65 As well as the token caching implementation guide https://claude.ai/public/artifacts/bcef31f7-3162-4176-93e2-4ff10f80d458

Step 3: Create 911 device user group with prioritization schemata and additional user group token caching permission Here is the user group and priority management system https://claude.ai/public/artifacts/9f5a6dfd-353f-4227-b205-372eedbc6b23 The group based cache sharing analysis https://claude.ai/public/artifacts/d6afb832-c4e5-4053-bc08-e44182e192b2 As well as the complete system integration for the public safety AI agent architecture https://claude.ai/public/artifacts/21700cdf-34db-4a6b-9ac0-25ed225b525f Including testing instructions for the application https://claude.ai/public/artifacts/e6586df2-2afd-4d1e-ae6b-dc31e01f0302

Step 4: Ask team mate 'Gemini' to critique and improve the public safety AI agent architecture Based on the feedback, here is an improved production ready architecture with HA/DR https://claude.ai/public/artifacts/d22dfb46-c41d-43d6-b89b-099bafc5b437 Including a refined production testing guide https://claude.ai/public/artifacts/74c0859a-ad3c-457f-beac-7ba7a66a946f

Step 5: Integrate the architecture with O-RAN RIC requirements Here is the complete architecture including the O-RAN RIC integration https://claude.ai/public/artifacts/0d96d0cf-3859-49e2-9151-51f5e7e9c127

Step 6: Optimize with Gemini for zero trust architecture (ZTA), post quantum encryption (PQE), and quantum key distribution (QKD) for future updates

The images below describes the complete production-ready architecture combining:

  1. ✅ NVIDIA NIM Integration (LLaMA 3.1 Nemotron + Embedding NIM)
  2. ✅ AWS EKS Deployment (Scalable GPU infrastructure)
  3. ✅ Agentic AI System (Autonomous handover decisions)
  4. ✅ User Groups & RBAC (Priority management for 911 services)
  5. ✅ Token Caching (70%+ cost reduction)
  6. ✅ Group Cache Sharing (Additional 20-30% savings)

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