Tool-based agent memory: why 2026 benchmarks favor it
Tool-based agent memory exposes store, retrieve, and navigate as callable MCP tools. 2026 benchmarks from Mem0, Memanto, and Wire show why the pattern wins.
Tool-based agent memory exposes store, retrieve, and navigate as callable MCP tools. 2026 benchmarks from Mem0, Memanto, and Wire show why the pattern wins.
AI support replies sound generic because teams treat brand voice as a prompt problem. Context engineering fixes it by selecting the right exemplars.
TOON looks more compact than JSON, but a 9,649-test study found it cost LLMs 38% more tokens. The reason: model training distribution beats format size.
OpenAI's GPT-5.5 system card reports 23% better claim-level accuracy, not the 60% hallucination reduction making press rounds. Here's what actually changed.
Agent drift is how AI agents silently deviate from goals over long-running tasks. Six mechanisms cause it, and most have nothing to do with the model.
Retrieval provenance for AI agents isn't an audit log or a trust verdict. It's structural metadata (source, position, time, edges) agents use to plan.
AI token usage scales with knowledge base size only when the full corpus loads per query. The real variable is selective context delivery, not KB size.
We restructured Wire's MCP surface from 2 overloaded tools to 3 single-purpose ones. The counterintuitive result: adding a tool cut total calls 24%.
Vectara's 2026 benchmark shows OpenAI's flagship GPT-5.4-pro hallucinates at 8.3% while its nano variant stays at 3.1%. The reasoning-model tradeoff, explained.
Native Notion and Obsidian MCP give every connected agent the same coarse scope. Build a private AI second brain with per-agent, revocable access across tools.