u/AntelopeGlobal6041

My AI agent kept forgetting the same rogue transmitter, so I gave it memory

I was building an SDR-based HF spectrum monitoring system that detects anomalous radio transmissions in real time.

But I ran into an unexpected issue:

Every time the same rogue transmitter appeared again days later, the agent treated it like a completely new event.

No memory.
No context.
No persistence.

It could detect anomalies, but it couldn’t recognize recurrence.

So I started experimenting with memory layers for the agent.

Now the system:

  • stores transmission fingerprints
  • compares new detections against historical anomalies
  • recognizes recurring burst patterns
  • tracks persistence across time/location windows
  • reduces repeated false escalations

The project is called TarangWatch — a distributed autonomous HF spectrum audit + intelligence platform.

I wrote about:

  • why stateless agents fail in long-running monitoring systems
  • SDR + anomaly detection workflow
  • how memory changes agent behavior
  • architecture decisions behind the system

Article:
https://medium.com/@manyarolekar/my-agent-kept-forgetting-the-same-rogue-transmitter-so-i-gave-it-a-memory-9b2a846b9298

Repo:
https://github.com/manyarolekar/tarang4all

Would love feedback from people working on:

  • agent memory
  • anomaly detection
  • SDR/signal intelligence
  • long-running autonomous systems
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u/AntelopeGlobal6041 — 3 days ago