Declarative Skills for AI Agents in Knowledge-Grounded Tool-Use Workflows
Quick Answer
This study evaluates declarative AI agents against imperative ones in customer-service workflows, revealing that declarative skills enhance accuracy and reduce errors under high-quality retrieval, while imperative agents struggle with task success.
Quick Take
The analysis includes three agent types formalized in a decentralized Markov decision process, highlighting retrieval quality as a critical bottleneck.
Key Points
- Declarative agents use natural-language skill files for improved orchestration in workflows.
- Retrieval quality significantly impacts all agent types, leading to performance degradation.
- Under high-quality retrieval, declarative skills enhance procedural task accuracy.
- Imperative agents show brittleness, failing to reliably improve task success rates.
- The study formalizes agents within a decentralized partially-observable Markov decision process.
Paper Resources
Source Excerpt
arXiv:2606. 06923v1 Announce Type: new Abstract: We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base. We argue that declarative agents -- AI agents equipped with natural-language skill files appended to the system prompt -- are an effective orchestration paradigm.
Concretely, we compare (i) a DeclarativeAgent that reads three domain-specific skill files at inference time and decides its own control flow, (ii) an ImperativeAgent based on a programmatic state machine with explicit phases, and (iii) an unscaffolded baseline agent modeled after the $\tau$-Knowledge benchmark agent. …
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