Technology
Oct 4, 2025
Building a Data Layer for Agent Reasoning
Building a Data Layer for Agent Reasoning
Building a Data Layer for Agent Reasoning
The next generation of AI agents doesn’t just query data - it reasons with it. Yet, most of today’s data layers are built for a world of filters, not meaning. While AI models have evolved to understand context, intent, and nuance, the underlying data systems still speak SQL - a language of rigid filters and static schemas. It’s like training a philosopher to think only in spreadsheets.
Aditya Lahiri
Aditya Lahiri
Aditya Lahiri
Co-Founder & CTO @ OpenFunnel
Understanding the Problem: Filters vs. Meaning
Traditional data pipelines assume humans or dashboards are the end consumers. So, they rely on text-to-SQL conversions - mapping natural language into pre-set filters and tables.
But agents don’t think in filters. They think in meaning.
Agents combine:
World knowledge: everything they’ve learned across domains.
User context: what the current task, company, or goal is.
They don’t ask, “Headcount > 500 AND Location = San Francisco.”
They ask, “Which companies are scaling their AI teams to serve enterprise clients?”
The intent is semantic - not structural.
The Industry Gap
The industry, however, is stuck in 2023. Most data providers still force agents to query pre-defined schemas, static filters, and rigid field mappings.
As a result, even the smartest agents end up bottlenecked by systems that don’t speak their language. This gap between reasoning and retrieval is what inspired us to rethink the GTM data layer from scratch.
Introducing an Agent-Native Data Layer for GTM
We designed a meaning-first data layer that enables agents to reason over both static and semantic dimensions.
Vector Embeddings + Semantic Matching
Instead of relying only on keyword filters, we use vector embeddings to represent company activities in a high-dimensional semantic space.
This allows agents to ask context-rich questions like:
What functions are they building out?
What migrations are happening?
What kind of people are joining or leaving?
What does this activity signal about their priorities?
Each query is interpreted based on meaning, not syntax.
Static Filters for Deterministic Fields
Not everything needs to be semantic. For stable, factual data like headcount, location, or funding stage - we retain traditional static filters.
The architecture blends semantic matching and structured querying, giving agents the best of both worlds.
The Architectural Bet
Our hypothesis is simple:
Agents are now intelligent enough to reason across both static and semantic fields.
The limitation isn’t their intelligence - it’s the data layer’s architecture.
So instead of forcing agents to adapt to outdated schemas, we built a schema that adapts to how agents think.
Beyond Keyword Search
Most companies are still optimizing for keyword search and text-to-SQL conversions.
We’re building for a future where agents truly understand meaning - not just match patterns.
In other words, the future of data isn’t query-driven.
It’s context-driven.
The next wave of GTM intelligence won’t come from better filters - it’ll come from data layers designed for agents that reason, not just retrieve.
Understanding the Problem: Filters vs. Meaning
Traditional data pipelines assume humans or dashboards are the end consumers. So, they rely on text-to-SQL conversions - mapping natural language into pre-set filters and tables.
But agents don’t think in filters. They think in meaning.
Agents combine:
World knowledge: everything they’ve learned across domains.
User context: what the current task, company, or goal is.
They don’t ask, “Headcount > 500 AND Location = San Francisco.”
They ask, “Which companies are scaling their AI teams to serve enterprise clients?”
The intent is semantic - not structural.
The Industry Gap
The industry, however, is stuck in 2023. Most data providers still force agents to query pre-defined schemas, static filters, and rigid field mappings.
As a result, even the smartest agents end up bottlenecked by systems that don’t speak their language. This gap between reasoning and retrieval is what inspired us to rethink the GTM data layer from scratch.
Introducing an Agent-Native Data Layer for GTM
We designed a meaning-first data layer that enables agents to reason over both static and semantic dimensions.
Vector Embeddings + Semantic Matching
Instead of relying only on keyword filters, we use vector embeddings to represent company activities in a high-dimensional semantic space.
This allows agents to ask context-rich questions like:
What functions are they building out?
What migrations are happening?
What kind of people are joining or leaving?
What does this activity signal about their priorities?
Each query is interpreted based on meaning, not syntax.
Static Filters for Deterministic Fields
Not everything needs to be semantic. For stable, factual data like headcount, location, or funding stage - we retain traditional static filters.
The architecture blends semantic matching and structured querying, giving agents the best of both worlds.
The Architectural Bet
Our hypothesis is simple:
Agents are now intelligent enough to reason across both static and semantic fields.
The limitation isn’t their intelligence - it’s the data layer’s architecture.
So instead of forcing agents to adapt to outdated schemas, we built a schema that adapts to how agents think.
Beyond Keyword Search
Most companies are still optimizing for keyword search and text-to-SQL conversions.
We’re building for a future where agents truly understand meaning - not just match patterns.
In other words, the future of data isn’t query-driven.
It’s context-driven.
The next wave of GTM intelligence won’t come from better filters - it’ll come from data layers designed for agents that reason, not just retrieve.
Understanding the Problem: Filters vs. Meaning
Traditional data pipelines assume humans or dashboards are the end consumers. So, they rely on text-to-SQL conversions - mapping natural language into pre-set filters and tables.
But agents don’t think in filters. They think in meaning.
Agents combine:
World knowledge: everything they’ve learned across domains.
User context: what the current task, company, or goal is.
They don’t ask, “Headcount > 500 AND Location = San Francisco.”
They ask, “Which companies are scaling their AI teams to serve enterprise clients?”
The intent is semantic - not structural.
The Industry Gap
The industry, however, is stuck in 2023. Most data providers still force agents to query pre-defined schemas, static filters, and rigid field mappings.
As a result, even the smartest agents end up bottlenecked by systems that don’t speak their language. This gap between reasoning and retrieval is what inspired us to rethink the GTM data layer from scratch.
Introducing an Agent-Native Data Layer for GTM
We designed a meaning-first data layer that enables agents to reason over both static and semantic dimensions.
Vector Embeddings + Semantic Matching
Instead of relying only on keyword filters, we use vector embeddings to represent company activities in a high-dimensional semantic space.
This allows agents to ask context-rich questions like:
What functions are they building out?
What migrations are happening?
What kind of people are joining or leaving?
What does this activity signal about their priorities?
Each query is interpreted based on meaning, not syntax.
Static Filters for Deterministic Fields
Not everything needs to be semantic. For stable, factual data like headcount, location, or funding stage - we retain traditional static filters.
The architecture blends semantic matching and structured querying, giving agents the best of both worlds.
The Architectural Bet
Our hypothesis is simple:
Agents are now intelligent enough to reason across both static and semantic fields.
The limitation isn’t their intelligence - it’s the data layer’s architecture.
So instead of forcing agents to adapt to outdated schemas, we built a schema that adapts to how agents think.
Beyond Keyword Search
Most companies are still optimizing for keyword search and text-to-SQL conversions.
We’re building for a future where agents truly understand meaning - not just match patterns.
In other words, the future of data isn’t query-driven.
It’s context-driven.
The next wave of GTM intelligence won’t come from better filters - it’ll come from data layers designed for agents that reason, not just retrieve.
Understanding the Problem: Filters vs. Meaning
Traditional data pipelines assume humans or dashboards are the end consumers. So, they rely on text-to-SQL conversions - mapping natural language into pre-set filters and tables.
But agents don’t think in filters. They think in meaning.
Agents combine:
World knowledge: everything they’ve learned across domains.
User context: what the current task, company, or goal is.
They don’t ask, “Headcount > 500 AND Location = San Francisco.”
They ask, “Which companies are scaling their AI teams to serve enterprise clients?”
The intent is semantic - not structural.
The Industry Gap
The industry, however, is stuck in 2023. Most data providers still force agents to query pre-defined schemas, static filters, and rigid field mappings.
As a result, even the smartest agents end up bottlenecked by systems that don’t speak their language. This gap between reasoning and retrieval is what inspired us to rethink the GTM data layer from scratch.
Introducing an Agent-Native Data Layer for GTM
We designed a meaning-first data layer that enables agents to reason over both static and semantic dimensions.
Vector Embeddings + Semantic Matching
Instead of relying only on keyword filters, we use vector embeddings to represent company activities in a high-dimensional semantic space.
This allows agents to ask context-rich questions like:
What functions are they building out?
What migrations are happening?
What kind of people are joining or leaving?
What does this activity signal about their priorities?
Each query is interpreted based on meaning, not syntax.
Static Filters for Deterministic Fields
Not everything needs to be semantic. For stable, factual data like headcount, location, or funding stage - we retain traditional static filters.
The architecture blends semantic matching and structured querying, giving agents the best of both worlds.
The Architectural Bet
Our hypothesis is simple:
Agents are now intelligent enough to reason across both static and semantic fields.
The limitation isn’t their intelligence - it’s the data layer’s architecture.
So instead of forcing agents to adapt to outdated schemas, we built a schema that adapts to how agents think.
Beyond Keyword Search
Most companies are still optimizing for keyword search and text-to-SQL conversions.
We’re building for a future where agents truly understand meaning - not just match patterns.
In other words, the future of data isn’t query-driven.
It’s context-driven.
The next wave of GTM intelligence won’t come from better filters - it’ll come from data layers designed for agents that reason, not just retrieve.
