A recent MIT study found that 95% of pilots fail because agents don't adapt and learn on the job. ALTK-Evolve addresses this learning gap using long term episodic memory to help agents reason better.

Evolve is a memory system for AI agents, that can help agents improve over time, learning from and using guidelines generated from previous executions.

We evaluated the framework on AppWorld, where agents complete realistic multi‑step tasks via APIs, averaging 9.5 APIs across 1.8 apps, with hard cases requiring more complex control flow. A ReAct agent received the task instruction plus the top 5 retrieved guidelines generated on a prior run (train/dev) and tested on an unseen partition (test-normal). We report Scenario Goal Completion (SGC), a strict consistency metric requiring success across variants.

Find more details about the experiments in the paper at https://arxiv.org/abs/2603.10600.