Context Graphs in Production: Happy Writers, Happy Readers
11 hours ago
- #context pipelines
- #data infrastructure
- #agent scale
- The article discusses the principle of 'reader-serving' writing, which shifts cognitive load from the reader to the writer through upfront structuring and editing.
- It draws a parallel to engineering context pipelines for AI agents, highlighting a tradeoff between work done at write time versus read time.
- Relational databases are described as writer-serving, requiring agents to perform joins and transformations at read time, while search indexes and document stores are reader-serving but face challenges in keeping data fresh at scale.
- At agent scale, maintaining fresh and correct context for numerous agents becomes untenable due to evolving data and retrieval patterns.
- The solution proposed is a 'live context graph'—a library of up-to-date, trustworthy contextual building blocks that serve both writers and readers efficiently.
- An incremental transform layer is suggested to convert unstructured writes into real-time, governed data products with semantic links, enabling composable building blocks for agents.
- Reducing write-to-context latency to single-digit seconds allows agents to become interactive, supporting tight feedback loops and more reliable experiences.
- This approach lets writers be self-indulgent while readers assemble context quickly and token-efficiently, with heavy lifting handled by the live context graph.