🤖 AI Summary
This work addresses reliability challenges in agent systems—such as hallucinated actions, infeasible plans, and fragile coordination—often stemming from the absence of explicit architectural constraints. To mitigate these issues, the authors propose DALIA, a declarative, model-agnostic agent architecture layer that enforces a clear separation between discovery, planning, and execution phases. DALIA leverages declarative capability specifications, a task discovery protocol, a federated agent registry, and deterministic task graphs to confine agent behavior within a verifiable action space. Notably, it introduces the first declarative architecture layer integrated with the Model-Controller-Planner (MCP) ecosystem, enabling reproducible and verifiable task-oriented workflows in heterogeneous environments. This approach significantly reduces reliance on speculative reasoning and unstructured coordination, thereby enhancing system reliability and cross-environment consistency.
📝 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.