Definition
Agent amnesia is the failure mode where AI agents lose state between turns or tool calls, forgetting prior decisions, prior tool-call results, or earlier user constraints, and re-deriving information they already had. It is distinct from hallucination — the information is not made up; it is just lost.
In Depth
Cross-turn memory failures show up in two flavors: short-term (within one user task, the agent forgets which tool it called three steps ago) and long-term (across user sessions, the agent doesn't recall prior preferences or completed work). The fixes split similarly: short-term needs state machines like LangGraph's checkpointer; long-term needs memory frameworks like Mem0 or Letta (formerly MemGPT). An r/LangChain post in April 2026 framed the problem as 'amnesic agents' and shipped a routing layer that improved task success rates measurably. Scavio doesn't solve memory directly; it complements memory frameworks by exposing semantically-named tools (search, reddit_search, youtube_search) so the routing decisions the agent recalls are unambiguous.
Example Usage
After adding LangGraph checkpoint state and renaming tools from 'search_v1' to semantic names ('reddit_search', 'youtube_search'), the agent's task success rate on 5-step research jumped from 48% to 94% in the post's reported benchmark. The 'amnesic' label was the diagnostic; the fix was state plus naming.
Platforms
Agent Amnesia (Cross-Turn Memory Failure) is relevant across the following platforms, all accessible through Scavio's unified API:
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