Towards a Declarative Agentic Layer for Intelligent Agents in MCP-Based Server Ecosystems

TL;DR

This paper introduces DALIA, a declarative architectural layer for intelligent agents that improves reliability and verifiability by formalizing capabilities, discovery, and task execution in multi-agent systems.

Contribution

It proposes a novel, model-independent architectural layer that formalizes agent capabilities and workflows, enhancing reliability and reproducibility in multi-agent systems.

Findings

DALIA enables verifiable agentic workflows across heterogeneous environments.

The architecture reduces reliance on speculative reasoning and free-form coordination.

Demonstrated through a representative task-oriented scenario.

Abstract

Recent advances in Large Language Models (LLMs) have enabled the development of increasingly complex agentic and multi-agent systems capable of planning, tool use and task decomposition. However, empirical evidence shows that many of these systems suffer from fundamental reliability issues, including hallucinated actions, unexecutable plans and brittle coordination. Crucially, these failures do not stem from limitations of the underlying models themselves, but from the absence of explicit architectural structure linking goals, capabilities and execution. This paper presents a declarative, model-independent architectural layer for grounded agentic workflows that addresses this gap. The proposed layer, referred to as DALIA (Declarative Agentic Layer for Intelligent Agents), formalises executable capabilities, exposes tasks through a declarative discovery protocol, maintains a federated directory of agents and their execution resources, and constructs deterministic task graphs grounded exclusively in declared operations. By enforcing a clear separation between discovery, planning and execution, the architecture constrains agent behaviour to a verifiable operational space, reducing reliance on speculative reasoning and free-form coordination. We present the architecture and design principles of the proposed layer and illustrate its operation through a representative task-oriented scenario, demonstrating how declarative grounding enables reproducible and verifiable agentic workflows across heterogeneous environments.