Why your platform team shouldn't build an AI context layer (even though they probably could)

Most engineering teams think they can build an internal AI knowledge system using standard tools and techniques. They envision setting up a vector database, connecting a few data sources, and running a retrieval augmented generation (RAG) pipeline over their documentation. While the database component might be straightforward, the larger challenge lies in creating a governed knowledge pipeline.

This goes beyond simple data ingestion and requires robust trust, compliance, and continuous maintenance measures. These elements compound with every new data source, stale documentation, and additional AI agents integrated into the system.

Building the pipeline involves several stages, each of which requires ongoing effort. Setting up connectors to integrate new data sources may seem manageable, but keeping these connectors up-to-date as vendor APIs evolve presents a significant challenge. Moreover, simply capturing a copy of the source is insufficient. Retrieval systems struggle to accurately extract specific sections from raw data sources like Slack threads.

The solution is to create durable knowledge nodes that contain the essence of the information, including its origin, ownership, and trustworthiness. If the source disappears, this knowledge remains intact. Ignoring this aspect is a critical mistake in building enterprise AI context layers.

Validating knowledge nodes involves two modes: implicit trust, which is automatic, and explicit trust, which requires human oversight for high-stakes decisions. Each knowledge node is assigned a trust score based on factors like authorship, usage, recency, source reliability, and potential conflicts with other information. Nodes that were accurate in the past may become outdated, necessitating periodic reviews to ensure they remain reliable. The key metric here is how often human intervention is required to maintain trust levels.

Organizing knowledge requires defining scopes that specify what information is relevant and who has access to it. This organizational layer helps ensure that knowledge is presented in the appropriate context and adheres to the relevant access permissions. By default, knowledge is personal and private, inheriting appropriate permissions from the user's context when accessed or composed. This approach prevents accidental leakage of sensitive information while allowing developers to maintain their own knowledge bases.

For enterprises, governance of AI systems must be integrated seamlessly with existing IT and security protocols. Security teams need to verify access and usage patterns without introducing additional layers of complexity. The governance system should provide both manual oversight for administrators and automated checks for end-users. Proper governance ensures that AI outputs can be traced back to validated, trustworthy sources, satisfying compliance requirements.

Ultimately, the decision to invest heavily in building an AI context layer depends on the strategic goals of the organization. If knowledge management is considered a competitive advantage, then dedicating engineering resources to maintain the pipeline is justified. However, for many product companies and developer platforms, maintaining a reliable and trustworthy pipeline should be a priority without consuming excessive engineering cycles.