Enabling spatial reasoning for LLMs

In previous articles we explored why large language models (LLMs) struggle with geographic reasoning and discussed two broad approaches to addressing this limitation: enriching the model with spatial knowledge and executing spatial reasoning through dedicated systems.

In practice, the most capable systems go further than combining these approaches. They draw a clear architectural line between what a language model should do and what it should never attempt.

The distinction that matters

Before examining the architecture, it is worth clarifying a common source of confusion.

"Grounding" has become a widely used term in artificial intelligence (AI) systems, but it typically refers to verification—giving a language model access to external sources so its outputs can be checked or supplemented. In this pattern, the model still attempts to reason about the question itself. The external system confirms or corrects the result.

What a location reasoning layer does is different in kind, not just in degree. Rather than verifying what the language model produced, it prevents the language model from attempting spatial computation in the first place.

The LLM recognizes that a query involves spatial logic, delegates it entirely to the execution layer and receives back a structured, validated result. The model never approximates a route, estimates a midpoint or infers a constraint. It hands the spatial problem to the system built to solve it.

This is offloading, not grounding. The difference matters because offloading removes the source of error rather than catching it after the fact.

What the execution layer handles

Consider a driver asking an in-vehicle assistant a simple-sounding question:

"I need to stop for fuel before the motorway section, but only somewhere a seven-meter van can access."

Handling a query like this requires:

• computing the current route to identify the motorway entry point

• searching for fuel stations before that point

• filtering by vehicle access constraints specific to that vehicle type

• returning options ranked by actual travel time, not straight-line distance

None of these steps involve language. Each requires computation over a road network, real-time location data and map attributes that encode physical constraints.

The execution layer handles what it is designed for: routing across road graphs, spatial search against live data, constraint validation against authoritative map attributes and multi-step operations that would otherwise require the model to chain many individual tool calls in sequence.

The result is not only more accurate. It is more efficient. Queries that previously required multiple round trips between the model and individual location APIs can be resolved in a single structured execution, with lower latency and lower token cost.

The compounding problem

Without accurate location context, organizations adopting agentic AI are already hitting the limits of what these systems can deliver:

• False autonomy: agents appear intelligent but rely on humans to fix spatial blind spots

• Slower time to value: teams hard-code spatial logic or add manual guardrails, slowing down pilots and agentic initiatives

• Higher risk exposure: incorrect routing, non-compliance or unsafe actions in physical environments

• Missed use cases: entire classes of operational and optimization scenarios remain unaddressable

They are not edge cases. At operational scale, each one compounds.

Why this becomes critical at scale

A single imprecise spatial result is a minor inconvenience. Across tens of thousands of queries a day—in logistics operations, mobility services or fleet management—the gap between close enough and correct is the gap between a system that works and one that quietly accumulates errors.

Logistics operators cannot afford routes that violate vehicle restrictions. Field service platforms cannot accept estimated times of arrivals (ETAs) that ignore live road conditions. Automotive assistants cannot give drivers directions based on probabilistic inference.

These systems require spatial correctness, not spatial approximation. That is the requirement a dedicated execution layer is designed to meet.

The role this plays in agentic systems

As AI systems evolve from conversational interfaces into agents that perform real-world tasks autonomously, the requirement for reliable spatial execution becomes foundational rather than optional.

The architecture that works in this context is with the language model that understands intent and orchestrates action, paired with a spatial execution layer that computes location outcomes correctly, deterministically and at the speed the task requires.

HERE Location Reasoning is built as this execution layer—designed to be called by any agent, on any platform, using any language model for any query where location determines the outcome.