VQV Signal

DEVELOPING WATCH PRACTICAL

Connected Data London panel to cover the 'context layer' for AI agents

According to a post on X by @Connected_Data, a panel at Connected Data London on November 11–12 will examine the "context layer" for AI agents. The session features Gartner VP Analyst Afraz Jaffri, Graphwise president Atanas Kiryakov and Digital Science's Mark Hahnel, moderated by Maya Natarajan. The panelists are set to explain what a context layer contains and how the right context reaches an agent at the right time, ending with questions to ask vendors who say they can deliver one.

Source: Scobleizer News Pulse (X) · x.com Found via Robert Scoble’s Scobleizer News Pulse · a post on X, not independently verified Published 2026-10-30T21:40:00+00:00 Detected 2026-10-31T01:17:39+00:00
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According to a post on X by @Connected_Data, a panel at Connected Data London on November 11–12 will examine the "context layer" for AI agents. The session features Gartner VP Analyst Afraz Jaffri, Graphwise president Atanas Kiryakov and Digital Science's Mark Hahnel, moderated by Maya Natarajan. The panelists are set to explain what a context layer contains and how the right context reaches an agent at the right time, ending with questions to ask vendors who say they can deliver one.

AI-assisted summary based on the listed source.

Gartner VP Analyst Afraz Jaffri, Graphwise president Atanas Kiryakov and Digital Science's Mark Hahnel, moderated by Maya Natarajan, will unpack what a context layer contains and how the right context reaches an agent at the right time. They'll end with the questions to ask any vendor who says they can deliver...

For teams building or buying AI agents, the panel frames context delivery as a design question to evaluate, not only a choice of model. Its closing focus on vendor questions could give buyers a practical checklist. The post describes the session only and includes no product claims or results, so the discussion itself will show what criteria the speakers propose.

Signal Strength 86% Technical label WATCH Public Interest 22 Category DEVELOPING Reader Depth PRACTICAL

Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.

Public Interest components
Recognizable Entity Score 0 Practical Impact Score 0 Novelty Interest Score 70 Consequence Score 18 Curiosity Score 16 Shareability Score 41

VQV surfaced this signal because it is recent, relevant to AI Agents, connected to Scobleizer News Pulse (X).