Automated Creation and Enrichment Framework for Improved Invocation of Enterprise APIs as Tools
By: Prerna Agarwal , Himanshu Gupta , Soujanya Soni and more
Potential Business Impact:
Helps AI understand and use complicated computer tools.
Recent advancements in Large Language Models (LLMs) has lead to the development of agents capable of complex reasoning and interaction with external tools. In enterprise contexts, the effective use of such tools that are often enabled by application programming interfaces (APIs), is hindered by poor documentation, complex input or output schema, and large number of operations. These challenges make tool selection difficult and reduce the accuracy of payload formation by up to 25%. We propose ACE, an automated tool creation and enrichment framework that transforms enterprise APIs into LLM-compatible tools. ACE, (i) generates enriched tool specifications with parameter descriptions and examples to improve selection and invocation accuracy, and (ii) incorporates a dynamic shortlisting mechanism that filters relevant tools at runtime, reducing prompt complexity while maintaining scalability. We validate our framework on both proprietary and open-source APIs and demonstrate its integration with agentic frameworks. To the best of our knowledge, ACE is the first end-to-end framework that automates the creation, enrichment, and dynamic selection of enterprise API tools for LLM agents.
Similar Papers
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Machine Learning (CS)
Helps AI remember more details for better thinking.
PAACE: A Plan-Aware Automated Agent Context Engineering Framework
Artificial Intelligence
Helps AI remember more steps in its tasks.
FABRIC: Framework for Agent-Based Realistic Intelligence Creation
Artificial Intelligence
Teaches AI to use tools without human help.