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

TL;DR

The LLM Delegate Protocol is presented, an AI-native communication protocol introducing five mechanisms: rich delegate identity cards with quality hints and reasoning profiles, progressive payload modes with negotiation and fallback, and trust domains enforcing security boundaries at the protocol level.

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.

Figures (9)

• Figure 1: Feature comparison across agent communication protocols. LDP provides full support for all AI-native features, while A2A and MCP offer only partial transport-level security.
• Figure 2: Architecture of the LLM Delegate Protocol. The LDP router uses rich identity cards (model family, quality hints, reasoning profiles) to make intelligent routing decisions---sending easy tasks to lightweight models and hard tasks to capable models. Five protocol mechanisms work together: identity cards, payload negotiation, governed sessions, provenance tracking, and trust domains.
• Figure 3: Routing quality by task difficulty (RQ1). All conditions perform comparably; A2A slightly outperforms LDP overall, though no differences are statistically significant ($n{=}10$ per cell). Error bars show $\pm 1$ standard deviation.
• Figure 4: Routing latency by task difficulty (RQ1). LDP achieves ${\sim}12\times$ lower latency on easy tasks by routing to the lightweight llama3.2:3b model, while A2A selects heavier models.
• Figure 5: Payload mode efficiency (RQ2). Left: Semantic frames reduce token count by 37% vs. text ($p{=}0.031$). Right: Quality is comparable across all modes, indicating no observed information loss from structured payloads.