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Field notes/Community builds/Building Stateful AI Agents That Survive Session Kills

Building Stateful AI Agents That Survive Session Kills

Agent amnesia, solved: 84ms cold starts, snapshots as memory that carries prior experience, and forked sandboxes to evaluate candidate solutions in parallel.

GBGowtham Boyina·JUL 11, 2026·10 MIN READ·Community builds
SBX-01C4SBX-01E3SBX-0202SBX-0221SBX-0240SBX-025FSBX-027ESBX-029DSBX-02BCSBX-02DBSBX-02FASBX-0319SBX-0338SBX-0357SBX-0376[ RUNTIME: ACTIVE ] P50 2.45S · P99 4.12S · 5M/PROJECT

A pattern for agents that don't lose their minds when the session dies: treat snapshots as long-term memory, so a resumed agent picks up with the accumulated state of every previous run. Gowtham measures 84ms cold starts and uses forked sandboxes to evaluate several candidate solutions in parallel before committing to one.

We didn’t write this one — it’s Gowtham Boyina’s piece, published on Towards AI. The note above is ours; the full article is theirs.

Read the full piece on Towards AI ↗
GB
WRITTEN BYGowtham BoyinaCommunity · Towards AI
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