Build the Context Layer Before the Agent

About this episode

The episode discusses the importance of building a context layer in AI systems before deploying agents, highlighting examples from Atlassian, Airbnb, and others.

Atlassian spent three years connecting 150 billion organizational objects before the results appeared: 44% more accurate AI answers, 48% fewer tokens, a coding agent that reviewed 2 billion lines of code in two minutes. That’s the proof enterprises are pointing to when they argue that context graphs are the unlock. What the benchmark obscures is the order of operations — the graph had to exist before any of those numbers were possible. The reorganization bet is running in parallel, and it’s moving faster than the infrastructure. Airbnb’s CHRO is converting documentation to markdown, building skills libraries, mining meeting recordings before institutional memory disappears — five structural prerequisites before the first agent goes live. Meta is posting $26.8 billion in Q1 profit, laying off 8,000 people, and reporting “horrifically, historically low” employee morale. Both are restructuring around AI. Only one is sequencing it correctly. In AI customer experience, Twilio is working against a Qualtrics finding that 1 in 5 AI interactions delivers zero benefit. Rikki Singh’s diagnosis is precise: the orchestration layer is there, but it’s running without the context layer…

People in this episode

Host: Arpy Dragffy

Guest: Rikki Singh

Topics covered

• AI
• context layer
• organizational structure
• customer experience
• knowledge workers
• automation

Keywords

• context layer
• AI answers
• organizational objects
• FAQ automation
• knowledge workers
• customer experience

Mentioned in this episode

Organizations: Atlassian, Airbnb, Meta, Twilio, Qualtrics

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