The shift from human-oriented consumption to autonomous agent processing requires a fundamental rethink of data architecture. While humans excel at interpreting messy, semi-structured HTML, agents thrive on deterministic, strongly typed schemas.
At Mosaic, we’ve developed a dual-track delivery system that ensures both parties receive the optimal format without compromising on data integrity or latency. This post explores the technical underpinnings of our Agent-First schema design.
01. The Schema Bottleneck
Most datasets are built as flat CSVs or loose JSON blobs. This works for exploratory analysis but fails when integrated into a production agent pipeline. An agent needs to know exactly what null means in a specific context. Is it missing data, or an empty set?
We resolve this by enforcing Strict Type Inference at the ingestion layer. Our pipelines validate every entry against a predefined Protobuf definition before it ever hits our storage layer.
“Data is the fuel for the agentic era, but raw data is too volatile. We need refined, high-octane structures to prevent hallucination and logic drift.”
02. Context Windows & Token Efficiency
Agents are often constrained by token limits and context window costs. Delivering a 5MB JSON file is inefficient if the agent only needs the top 100 behavioral signals. Our API supports ?view=minimal, a feature that strips verbose human descriptions in favor of compact vector and categorical fields.
Consider the following snippet from our ads dataset:
{"id": "ads_992", "v": [0.12, -0.98, 0.44], "c": "cat_finance"}
03. Real-time Synchronization
The final pillar of our architecture is latency. Agents acting on stale market data are worse than useless—they are dangerous. We utilize a streaming layer that pushes updates to listeners as soon as a data point passes verification.
In short: building for agents is not only about better data. It’s about better delivery mechanisms.