AgentSpec: Understanding Embodied Agent Scaffolds Through Controlled Composition

2026-06-12

AI Summary

This work addresses the limitations of prevailing tightly coupled architectures in embodied agents, which hinder the disentanglement of individual module contributions and interaction effects. The authors propose AgentSpec, a framework that establishes the first standardized, modular specification for embodied agents, enabling flexible substitution and recombination of perception, memory, reasoning, reflection, action, and learning modules through typed composition and unified interfaces. Systematic evaluation across multiple benchmarks—including DeliveryBench, ALFRED, MiniGrid, and RoboTHOR—with diverse model backbones reveals that agent performance is primarily governed by scaffold compatibility and inter-module interactions rather than the strength of any single module. Key findings include the benefit of structured memory for long-horizon state tracking, environment-dependent synergy between reasoning and memory, a trade-off between error correction and computational cost in reflection mechanisms, and the necessity of co-optimizing reinforcement learning policies with deployment-time scaffolds for peak performance.

Abstract

LLM agents are increasingly built not as single model calls, but as scaffolded systems that combine reasoning, memory, reflection, action execution, and learning. While such scaffolds often improve performance, they are often embedded in tightly coupled pipelines, making it difficult to isolate component contributions, compare alternative designs, or understand how module interactions shape agent behavior. We introduce AgentSpec, a modular specification framework that represents embodied agents as typed compositions of reusable policy components with standardized interfaces. AgentSpec standardizes the interfaces among perception, memory, reasoning, reflection, action, and optional learning, enabling components to be swapped and recombined under controlled conditions. We instantiate this framework across DeliveryBench, ALFRED, MiniGrid, and RoboTHOR, and analyze reasoning, memory, reflection, and reinforcement-learning modules across model backbones. Our results show that agent performance is governed by scaffold compatibility and interaction effects rather than isolated module strength. In particular, structured multi-granularity memory improves long-horizon state tracking, reasoning and memory interact non-uniformly across environments, reflection trades off correction and cost, and RL-trained policies compose best when optimized with deployment-time scaffold structure. AgentSpec provides a controlled foundation for studying, comparing, and designing composable LLM agents. Our code, baselines and interactive playground are publicly available at https://agentspec-embodied.github.io.

Problem

• embodied agents
• modular scaffolds
• component interaction
• controlled composition
• agent behavior

Innovation

• modular specification
• composable agents
• scaffold compatibility
• standardized interfaces
• embodied AI