Temporal Context
Provide models with structured awareness of elapsed time, ordering, deadlines, and the evolving state of a process rather than leaving time implicit in the prompt.
Temporal reasoning infrastructure for agentic systems
Give AI agents a working model of time.
Admaria Time Co builds infrastructure for AI systems that need to understand not just what is true now, but what changes as time moves forward.
The Admaria Time Engine is an LLM middleware layer that gives AI agents a plugin means of understanding temporality and its side-effects. The engine enables AI models to understand the consequences of time passing within a system, so decisions can account for sequence, delay, expiry, dependency, and state change.
Plugin-Ready
Designed to slot into agent workflows as middleware.
Time-Aware
Models temporal consequences instead of isolated prompts.
System-Aware
Captures the side-effects of time passing within a system.
The Admaria Time Engine
Consequence Modeling
Help agents reason about what changes when time passes, which states expire, and what follow-on effects emerge across connected actions inside the host system.
Middleware Integration
Add temporal intelligence through a plugin-oriented middleware layer that sits between AI agents and the environments they act within.