Your AI agent works great. Then the phone gets warm. Then it doesn’t.

Description and use

On Android, continuous inference can cause device overheating and CPU throttling, leading to latency spikes. By proactively monitoring thermal headroom and throttling inference, the agent maintains a smoother experience.

Problem

On-device AI agents can become slow after a few minutes of continuous inference due to thermal throttling, which is not evident during testing.

Outcome

Proactive throttling based on real‑time thermal headroom prevents sudden latency spikes and maintains a smoother user experience on devices prone to thermal throttling.

Workflow

• 1. Call PowerManager.getThermalHeadroom(1) to obtain a 1‑second forecast of available thermal headroom.
• 2. If headroom < 0.3 f, decide on an action: < 0.2 f → switch to a lighter model; < 0.3 f → reduce inference frequency; < 0.5 f → skip non‑critical inference.
• 3. Implement the chosen action in the inference loop.
• 4. Continuously monitor headroom during sustained inference.
• 5. Test on a 3‑4 year‑old mid‑range device for 10 min to observe latency trends.
• 6. Use Firebase Test Lab to identify affected devices in the target market.

Evidence

Android’s thermal system throttles CPU and GPU frequency when the device temperature rises.

Source

PowerManager.getThermalHeadroom() gives you a 1-second forecast of available thermal headroom, from 0.0 (throttling now) to 1.0 (full capacity).

Source

When headroom drops below your threshold: ... switchToLighterModel(), reduceInferenceFrequency(), skipNonCriticalInference()

Source

Run sustained inference on a 3-4 year old mid-range device for 10 minutes. Watch the latency numbers. If they climb, you have a thermal problem.

Source