6 Layer Context System For Data

A six-layer context system for data agents extends the three-layer pattern with dedicated layers for glossary, tribal knowledge, and corrections log. It is what production deployments with thousands of tables and multiple domains converge on.

The three-layer pattern (schema, semantics, signals) is a great starting point. Once your warehouse grows past a few thousand tables and multiple domains, you hit a ceiling where the pattern stops scaling. The fix is to split further into six layers. This guide walks through each layer and why it matters. Compare to 3-layer context system for data and AI for data infrastructure.

The Six Layers

• •Layer 1 (Schema) — tables, columns, types, keys
• •Layer 2 (Semantics) — table and column descriptions, tags
• •Layer 3 (Glossary) — business terms with SQL templates
• •Layer 4 (Signals) — query logs, dashboards, canonicality
• •Layer 5 (Tribal) — captured knowledge from senior users
• •Layer 6 (Corrections) — past user feedback, scoped and decayed

Why Split Glossary From Semantics

Glossary entries are structurally different from semantic descriptions. A description is a free-text explanation of a table. A glossary entry is a SQL template mapping a business term to executable code. Merging them into one embedding index means the template gets treated like text and loses its executable property. Splitting the layer preserves the template as a first-class artifact.

The practical benefit is that the agent can instantiate a glossary template directly into its generated SQL. A description just provides context the model has to interpret; a template is code it can paste. The difference in accuracy is large.

Why Split Tribal From Corrections

Tribal knowledge is affirmative — this is how the expert does it. Corrections are negative — do not do it this way. They score differently in retrieval and have different decay rates. Tribal knowledge is long-lived because expert behavior changes slowly. Corrections are shorter-lived because they often reflect transient mistakes.

Keeping them in the same layer means one decay rate for both, which hurts one or the other. Splitting lets each have its own freshness curve and retrieval policy.

Retrieval Orchestration

Six layers means six retrieval calls. That sounds expensive until you realize each can run in parallel and most complete in under 50ms. The orchestrator launches all six, merges shortlists with weighted re-ranking, and hands the result to the generator. Total retrieval time is the max of the six, not the sum.

The weights come from training on historical corrections. When the agent gets a question right, the weights that surfaced the winning context get a small boost. When it gets a question wrong, the weights that surfaced the wrong context get a small penalty. Over time the weights converge on the right balance for your specific warehouse.

When Six Layers Are Needed

Six layers are overkill for small teams. The pattern pays off when you have thousands of tables, multiple domains with conflicting definitions, and an active corrections log with hundreds of entries. Below that scale, stick with three layers and skip the complexity.

A good signal that you need six layers is when corrections start conflicting with each other (different teams correct the same query different ways) or when the glossary grows past a few hundred entries. Those are the thresholds where the three-layer pattern starts to blur.

Storage and Operations

Six layers means six stores. That is fine — each is small, each has its own schema, and each can use the right technology. Schema and signals often live in Postgres. Semantics and tribal knowledge often live in a vector database. Glossary lives in a versioned git repo. Corrections live in an append-only log. The diversity is a feature, not a cost.

Common Mistakes

The worst mistake is adopting six layers before you need them. The second is merging glossary back into semantics to save a retrieval call. The third is giving every layer the same decay rate. The fourth is manual weighting without feedback-driven tuning.

Data Workers ships the six-layer pattern as the default for enterprise deployments and degrades to three layers for smaller teams. The switch is a configuration flag. To see it running on your scale, book a demo.

Migration from Three to Six Layers

Teams that start with three layers and grow into six do not rewrite their system. Migration is incremental. First split glossary out of semantics as its own layer. Run the old three-layer and new four-layer side by side on the benchmark, verify accuracy does not regress, promote. Then split tribal and corrections. Each split is a week of work and produces a measurable improvement.

The reason to migrate incrementally is risk containment. If splitting glossary hurts accuracy, you can roll back without affecting the other layers. If all layers are split at once, a regression in one layer looks like a regression in all layers and debugging is painful.

Data Workers supports both patterns out of the box and exposes the layer count as configuration. Teams flip the switch when they are ready and the system automatically picks up the new layers. No rewrites, no data migration, just a configuration change and a benchmark run.

When Layers Should Merge

Sometimes layers should merge, not split. If the corrections log is nearly empty and rarely retrieved, it can sit inside semantics as a minor component until usage grows. Premature splitting is as bad as premature merging. The trigger for splitting is real retrieval pressure, not architectural aesthetics.

Watch the retrieval logs. If a layer is retrieved on 80 percent of requests, it belongs as its own layer. If it is retrieved on 5 percent, it can be a sub-component. Adjust the architecture based on data, not opinion. Teams that split on opinion end up with unused layers and wasted complexity.

Data Workers tracks retrieval rate per layer and recommends split or merge based on thresholds. Teams see the recommendation and act on it. The architecture evolves with the warehouse, not stuck in a config from two years ago.

Six layers is the end state for large-scale data agent context. Schema, semantics, glossary, signals, tribal, corrections — each retrieved independently, each tuned by feedback. Start with three layers and graduate when you hit the ceiling.