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IBM Research introduces ALTK-Evolve for customer service agents

IBM Research presents ALTK-Evolve, a method that uses learned memory and prompt-appended playbooks to top customer service benchmarks without updating model weights.

IBM Research introduces ALTK-Evolve for customer service agents — image from Hugging Face
Image: Hugging Face

Summary

IBM Research details ALTK-Evolve, an approach that analyzes training task attempts to extract behavioral rules and appends them to an agent system prompt. Tested on the tau2-bench customer service benchmark, the method improves accuracy and reliability for both older and frontier models without requiring model fine-tuning or weight updates.

What changed

  • Uses ALTK-Evolve to distill agent successes and failures into short behavioral rules added to system prompts.
  • Improves agent performance and reliability on tau2-bench retail and airline customer service tasks.
  • Outperforms fine-tuned and frontier model entries on the tau2-bench leaderboard using prompt-level playbooks.

Availability

Available now

“- ALTK-Evolve library — https://github.com/AgentToolkit/altk-evolve”

Founder takeaway

If you build customer service agents, consider extracting behavioral guidelines from task attempts and appending them to system prompts to improve reliability without fine-tuning.

Source

Hugging FaceALTK-Evolve: How learned memory tops the τ²-bench leaderboard on customer service tasks without a frontier model

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