How a10y components map to 3GPP TS 23.288 (NWDAF) functional entities — and how Agent Server extends NWDAF into autonomous closed-loop operations.
| 3GPP Function | Spec | a10y Component | Role |
|---|---|---|---|
| DCCF | TS 23.288 | Vector | Data collection, normalization, routing from network functions |
| ADRF | TS 23.288 | OpenObserve + Qdrant | OpenObserve: raw telemetry storage. Qdrant: analytics results, patterns, embeddings |
| AnLF | TS 23.288 | correlation-engine (Claude Code) + Keep | Analytics inference, causal reasoning, alert correlation |
| MTLF | TS 23.288 | Qdrant + Claude | Learning from past incidents via RAG, in-context learning |
| MFAF | TS 23.288 | NATS | Event-driven messaging framework between all functions |
| NRF-like | TS 23.501 | active-inventory | Network topology, device registry, service discovery |
| FM / PM | TS 28.532 | Keep | Fault management, alert lifecycle, remediation workflow execution |
| Consumer NFs | — | aether-ide / aether-term | Analytics consumers, operator interfaces |
3GPP TS 23.288 defines NWDAF with two logical functions: AnLF (inference) and MTLF (training). In a10y, these are not monolithic — they are distributed across components, with Claude Code as the cognitive core.
Standard NWDAF is reactive — it responds to analytics requests from consumer NFs. Agent Server transforms it into an autonomous agent that proactively monitors, reasons, and acts.
| Mode | Reactive — responds to requests |
| Output | Analytics reports to consumer NFs |
| Loop | Open — human decides and acts |
| TMF Level | L1–L2 |
| Mode | Autonomous — event-driven |
| Output | RCA + remediation actions |
| Loop | Closed — detect, reason, act, verify |
| TMF Level | L3–L4 |
In standard 3GPP, AnLF is a passive function that sits behind a service-based interface, waiting for Nnwdaf requests. a10y redefines AnLF as an active agent:
NWDAF tells you what happened. a10y understands why and fixes it.