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LDP: An Identity-Aware Protocol for Multi-Agent LLM Systems

arXiv:2603.08852v1 Announce Type: new Abstract: As multi-agent AI systems grow in complexity, the protocols connecting them constrain their capabilities. Current protocols such as A2A and MCP do not expose model-level properties as first-class primitives, ignoring properties fundamental to effective delegation: model identity, reasoning profile, quality calibration, and cost characteristics. We present the LLM Delegate Protocol (LDP), an AI-native communication protocol introducing five mechanisms: (1) rich delegate identity cards with quality hints and reasoning profiles; (2) progressive payload modes with negotiation and fallback; (3) governed sessions with persistent context; (4) structured provenance tracking confidence and verification status; (5) trust domains enforcing security boundaries at the protocol level. We implement LDP as a plugin for the JamJet agent runtime and evaluate against A2A and random baselines using local Ollama models and LLM-as-judge evaluation. Identity-a

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Sunil Prakash · · 1 min read · 21 views
#cs.AI #cs.MA #cs.SE

arXiv:2603.08852v1 Announce Type: new Abstract: As multi-agent AI systems grow in complexity, the protocols connecting them constrain their capabilities. Current protocols such as A2A and MCP do not expose model-level properties as first-class primitives, ignoring properties fundamental to effective delegation: model identity, reasoning profile, quality calibration, and cost characteristics. We present the LLM Delegate Protocol (LDP), an AI-native communication protocol introducing five mechanisms: (1) rich delegate identity cards with quality hints and reasoning profiles; (2) progressive payload modes with negotiation and fallback; (3) governed sessions with persistent context; (4) structured provenance tracking confidence and verification status; (5) trust domains enforcing security boundaries at the protocol level. We implement LDP as a plugin for the JamJet agent runtime and evaluate against A2A and random baselines using local Ollama models and LLM-as-judge evaluation. Identity-aware routing achieves ~12x lower latency on easy tasks through delegate specialization, though it does not improve aggregate quality in our small delegate pool; semantic frame payloads reduce token count by 37% (p=0.031) with no observed quality loss; governed sessions eliminate 39% token overhead at 10 rounds; and noisy provenance degrades synthesis quality below the no-provenance baseline, arguing that confidence metadata is harmful without verification. Simulated analyses show architectural advantages in attack detection (96% vs. 6%) and failure recovery (100% vs. 35% completion). This paper contributes a protocol design, reference implementation, and initial evidence that AI-native protocol primitives enable more efficient and governable delegation.

Executive Summary

This article presents the LLM Delegate Protocol (LDP), a novel communication protocol designed for multi-agent Large Language Model (LLM) systems. LDP addresses the limitations of existing protocols by introducing five mechanisms: rich delegate identity cards, progressive payload modes, governed sessions, structured provenance tracking, and trust domains. The authors evaluate LDP against A2A and random baselines, demonstrating significant improvements in latency, token count reduction, and attack detection. While LDP shows promise, its effectiveness in improving aggregate quality is limited. The authors also highlight the importance of verification in confidence metadata. This research contributes to the development of AI-native protocol primitives, enabling more efficient and governable delegation in multi-agent LLM systems.

Key Points

  • ▸ LDP introduces five mechanisms to address limitations in existing protocols
  • ▸ Evaluations demonstrate significant improvements in latency, token count reduction, and attack detection
  • ▸ Verification is crucial for confidence metadata to avoid degradation in synthesis quality

Merits

Strength in Protocol Design

LDP's modular design and emphasis on AI-native protocol primitives enable more efficient and governable delegation, addressing critical limitations in existing protocols.

Demerits

Limitation in Aggregate Quality Improvement

Despite significant improvements in other areas, LDP's effectiveness in improving aggregate quality is limited, suggesting further refinement is necessary to achieve comprehensive benefits.

Dependence on Specific Implementation

The evaluation's reliance on the JamJet agent runtime and Ollama models may limit the generalizability of the results, highlighting the need for more comprehensive testing across diverse platforms and models.

Expert Commentary

While LDP shows remarkable potential, its limitations in aggregate quality improvement and dependence on specific implementation highlight the need for further refinement and testing. Nevertheless, this research marks a significant step forward in the development of AI-native protocol primitives, underscoring the importance of efficient and governable delegation in multi-agent LLM systems. As these technologies continue to evolve, it is essential to prioritize the development of robust, adaptable, and secure protocols that can meet the growing demands of complex AI systems.

Recommendations

  • ✓ Further evaluations should be conducted across diverse platforms, models, and scenarios to ensure the generalizability of LDP's results.
  • ✓ The development of regulatory frameworks should prioritize the unique challenges and opportunities arising from AI-native protocol primitives like LDP.

Sources

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