How to Keep Long-Running Agents on Track: Redis State Beats Context Stuffing
Deepfeet-09 · reddit · 2026-09-03
The author shares lessons from optimizing long-running agent workflows: the bottleneck is almost always memory and state management, not raw model capability.
- In long multi-step tool-call sessions, standard context windows overflow or suffer severe context rot.
- Feeding full prompt histories to GPT/Claude degraded performance after just a few dynamic interactions.
- Switching to stateful tracking — frameworks like Lyzr plus a custom Redis layer, confining agent memory to structured state instead of resending the whole conversation — greatly cut latency and token bloat.
- The author asks how others handle state persistence in complex agentic setups.
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