Research

Fast Memory, Slow Weights: A Production Architecture for Self-Improving AI Agents at the System Layer

Why production agents should learn continuously without continuously rewriting their models

Abstract. We run a multi-tenant AI-agent platform in production and tried to make its worker model learn from its own accumulated experience. The first consolidation attempt, two rank-16 LoRA adapters trained on 423 verified trajectory records, improved the behaviour it targeted and moved an untargeted behaviour the wrong way by more than our pre-registered gate allows. On 73 recovery states, exact correctness rose from 31 to 38 (7 adapter-only wins, 0 losses, p = 0.0156); on 160 known-failure states, exact repeats of a failed action fell from 20% to 15% (p = 0.0215); on 73 clean decisions, correctness fell from 49 to 45, a difference of +3/−7 discordant pairs (p = 0.34) that the suite cannot separate from noise but that fails a −2-point non-inferiority gate. The adapter was rejected by the gate we had written before training it. The lesson is not that fine-tuning forgets, which the literature predicts, but that at production sample sizes a single weight update produces gains and losses that cannot be told apart cheaply, and cannot be reversed separately. That is the case for learning on two timescales. A fast loop writes verified experience into an external memory that is scoped, editable, attributable and reversible, and changes behaviour on the next turn without touching weights; as of 20 September 2026 that loop holds 433 rules and 3,422 memory files across 74 workspaces, with 37 rules retired through a provenance log. A slow loop consolidates only patterns demonstrated repeatedly, through a curated training set, a frozen 484-decision evaluation, a nine-condition promotion gate and shadow deployment. We describe the implemented mechanisms, the production evidence, the failure modes the design does and does not address, and the sense in which the loop is recursive. We claim a practical architecture for recursive self-improvement at the agent and system layer under human-written gates, not a solution to continual learning and not model-level self-improvement. The effect of the fast loop on task outcomes has not yet been measured; the experiment that would measure it is specified.

@techreport{lazic2026fastmemory,
  title        = {Fast Memory, Slow Weights: A Production Architecture for Self-Improving AI Agents at the System Layer},
  author       = {Lazi\'c, Pavle},
  institution  = {ScalablyAI},
  type         = {Research / Production Field Report},
  number       = {SR-2026-001},
  year         = {2026},
  month        = sep,
  note         = {v1.0, sha256 8e2ef94c6a99b39b},
  url          = {https://scalably.io/research/fast-memory-slow-weights}
}