AI agents are getting powerful, but they still have a very basic problem: they keep relearning the same things.

Rohan Paul Twitter · Rohan Paul (@rohanpaul_ai) · 2026-06-03

Rohan Paul identifies repeated context-rebuilding across sessions as a fundamental inefficiency in current AI agent systems, pointing to EvoMapAI as an approach addressing persistent agent memory.

Open original ↗

Appears in

Research Findings Challenge AI Agent Architecture Assumptions

Extraction

Topics: ai-agentsagent-memorycontext-managementagentic-workflows

Claims

Current AI agents repeatedly rebuild the same context from scratch across sessions.
Coding agents such as Cursor lose accumulated context each time a new session begins.
Security triage agents suffer the same repeated context-building inefficiency.
Persistent memory across agent sessions is an unsolved problem that products like EvoMapAI are attempting to address.

Key quotes

AI agents are getting powerful, but they still have a very basic problem: they keep relearning the same things.

Every time you open a new Cursor session, run a coding agent, or ask an agent to triage security findings, a lot of the work is repeated context-building.