Abstract
The present disclosure relates to systems and methods for configuring the operational context of artificial intelligence (AI) agents using short, memorable text phrases. Large language models (LLMs) and agentic models often require precise contextual scoping to function effectively, as unrestricted access to vast data sources can lead to hallucinations or broad, inaccurate responses. The described technology utilizes a configuration alias, similar to a shortened uniform resource locator (URL), which is recognized by a parser within the AI interface. Instead of processing this alias as a standard prompt, the system performs a backend lookup to retrieve a pre-defined set of configuration parameters. These parameters dynamically establish the session’s scope by defining accessible data endpoints, selecting specific sub-agents, curating resource materials, and loading initial system prompts. This approach allows users to rapidly instantiate complex, pre-determined operational environments for AI interactions.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Rutowski, Tomek, "Dynamic Configuration of Artificial Intelligence Agent Contexts via Shortened Aliases", Technical Disclosure Commons, (February 12, 2026)
https://www.tdcommons.org/dpubs_series/9324