Agent-Controlled Forgetting for Tool-Using Agents: Reversible Context Curation in Practice

Key points

• Tool-using agents repeatedly carry observations whose useful content can be much smaller than their original payload.1
• A Python harness exposes batch archival and explicit recovery without task-specific model training, while protecting user instructions and assistant messages from these operations.1
• In an exploratory OpenTelemetry debugging case followed by an unrelated implementation task, the method ended with 231,951 provider-reported prompt tokens versus 912,492 under retained history, used 50% fewer cumulative input tokens, and had an estimated API cost of USD 1.28-1.44 versus approximately USD 4.38.1
• These observations demonstrate substantial resource savings in noisy tool-use trajectories and identify workload dependence as a central consideration for reversible context management.1

Sources (1)

• [1]Agent-Controlled Forgetting for Tool-Using Agents: Reversible Context Curation in Practice
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 06:13 PM

  We study agent-controlled forgetting: the acting model selects previously observed tool results, replaces each with a short note at its original position, and retains the exact original in a recoverable archive.

  Tool-using agents repeatedly carry observations whose useful content can be much smaller than their original payload.

Extractive summary: sentences quoted from the sources.