Search papers, labs, and topics across Lattice.
The paper introduces the LLM Delegate Protocol (LDP), a novel communication protocol for multi-agent LLM systems that incorporates model identity, reasoning profiles, and cost characteristics as first-class primitives. LDP features rich delegate identity cards, progressive payload modes, governed sessions, structured provenance tracking, and trust domains. Evaluations against A2A and random baselines demonstrate significant latency reductions on easy tasks via delegate specialization and token count reduction through semantic frame payloads, although noisy provenance degrades synthesis quality.
Multi-agent LLM systems can achieve 12x lower latency by routing tasks based on delegate identity, but beware: injecting confidence metadata without verification can backfire and hurt quality.
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.
