AECP: Artifact-Exclusive Communication Protocol for Multi-Agent Code Generation

Jiaqi Xue; Yanjun Wang; Xiangci Li; Lingbo Mo; Aritra Sengupta; Shweta Garg; Murali Krishna Ramanathan; Myeongsoo Kim

cs.CLOct 5, 2026

AECP: Artifact-Exclusive Communication Protocol for Multi-Agent Code Generation

Authors: Jiaqi Xue, Yanjun Wang, Xiangci Li, Lingbo Mo, Aritra Sengupta, Shweta Garg, Murali Krishna Ramanathan, Myeongsoo Kim

Organizations: University of Central Florida · AWS AI Labs

Abstract

As AI agents increasingly tackle complex repository-level coding tasks, distributing work across multiple agents is a natural way to scale beyond the capabilities of a single agent. To coordinate their interdependent work, these agents share findings and agree on interfaces between modules. However, exchanged information often serves only as context, leaving individual agents to interpret it and incorporate it into subsequent work. Consequently, shared findings may go unused and deviations from interface agreements may go undetected, undermining the reliability and efficiency of collaboration. This motivates moving part of the coordination responsibility from individual agents to the execution harness. To make shared information actionable during execution, we introduce the Artifact-Exclusive Communication Protocol (AECP). AECP requires agents to communicate exclusively through structured artifacts and specifies how the harness processes them. The harness supplies findings when agents access relevant code, screens implementations for mismatches with recorded interface commitments, and requires affected agents to revisit revised agreements. These coordination steps become part of harness execution rather than actions that agents must initiate from prior messages. Across Doc2Repo, NL2Repo, and CodeProjectEval, using closed- and open-source models including Opus-4.8 and DeepSeek-V4-Flash, AECP improves average test pass rate by 28.2% and reduces average wall time by 16.5% relative to an agent team using free-form inter-agent messages. Artifact-exclusive communication also blocks the relay of malicious instructions between agents, reducing how often they reach other agents from 95% to 0% and how often those agents act on them from 40% to 0%.

Figures & tables

Figure 1: Multi-agent coordination and development overhead. (a) Agents interpret exchanged messages and decide how to act on them; shared plans and agreements may be followed, deviated from, or ignored. (b) AECP requires exchanges to pass through structured artifacts, enabling the execution harness to guide agents with shared knowledge and check implementations against recorded agreements. (c) Token and interaction-turn distributions for the natural-language communication baseline across 104 NL2Repo tasks, estimated from LLM-labeled trajectories (Appendix G ).

Figure 2: System overview of AECP. (a) The leader converts project specifications into a module plan and assigns modules to workers based on their dependencies. (b) Workers explore, implement, and test their assigned modules in parallel, sharing findings and interface commitments through artifacts. (c) The execution harness uses Knowledge Artifacts to supply relevant findings at the point of use and Contract Artifacts to support parallel development and screen implementations for mismatches with interface commitments.

Figure 3: Scope-triggered knowledge sharing. Worker A records a reusable finding together with its applicable scope. When Worker B accesses the corresponding code, the harness matches the current scope against stored artifacts and includes the relevant finding in the returned observation, making it available for subsequent reasoning and implementation.

Figure 4: Contract Sharing for parallel development. Worker A declares an interface that the harness exposes as importable stubs for Workers B and C. The harness screens the producer’s completed implementation for static mismatches with the latest contract. When the interface changes, it identifies affected consumers through registered dependencies and requires them to revisit the revised contract before completing their tasks.

Model	Method	Doc2Repo			NL2Repo			CodeProjectEval
Model	Method	Score	Time (s)	Token (M)	Score	Time (s)	Token (M)	Score	Time (s)	Token (M)
Opus -4.8	Single	$0.71_{{\color[rgb]{0.5,0.5,0.5}\pm 0.02}}$	$282_{{\color[rgb]{0.5,0.5,0.5}\pm 83}}$	$7.06_{{\color[rgb]{0.5,0.5,0.5}\pm 1.12}}$	$0.65_{{\color[rgb]{0.5,0.5,0.5}\pm 0.04}}$	$868_{{\color[rgb]{0.5,0.5,0.5}\pm 97}}$	$7.49_{{\color[rgb]{0.5,0.5,0.5}\pm 0.62}}$	$0.41_{{\color[rgb]{0.5,0.5,0.5}\pm 0.05}}$	$894_{{\color[rgb]{0.5,0.5,0.5}\pm 126}}$	$4.89_{{\color[rgb]{0.5,0.5,0.5}\pm 0.29}}$
	Team-Freedom	$0.52_{{\color[rgb]{0.5,0.5,0.5}\pm 0.05}}$	$505_{{\color[rgb]{0.5,0.5,0.5}\pm 146}}$	$8.71_{{\color[rgb]{0.5,0.5,0.5}\pm 0.38}}$	$0.51_{{\color[rgb]{0.5,0.5,0.5}\pm 0.03}}$	$1{,}194_{{\color[rgb]{0.5,0.5,0.5}\pm 71}}$	$6.90_{{\color[rgb]{0.5,0.5,0.5}\pm 1.31}}$	$0.25_{{\color[rgb]{0.5,0.5,0.5}\pm 0.02}}$	$1{,}038_{{\color[rgb]{0.5,0.5,0.5}\pm 94}}$	$5.10_{{\color[rgb]{0.5,0.5,0.5}\pm 0.87}}$
	Team-Integrator	$0.68_{{\color[rgb]{0.5,0.5,0.5}\pm 0.02}}$	$607_{{\color[rgb]{0.5,0.5,0.5}\pm 88}}$	$18.57_{{\color[rgb]{0.5,0.5,0.5}\pm 1.57}}$	$0.66_{{\color[rgb]{0.5,0.5,0.5}\pm 0.05}}$	$1{,}753_{{\color[rgb]{0.5,0.5,0.5}\pm 119}}$	$10.45_{{\color[rgb]{0.5,0.5,0.5}\pm 0.44}}$	$0.42_{{\color[rgb]{0.5,0.5,0.5}\pm 0.04}}$	$830_{{\color[rgb]{0.5,0.5,0.5}\pm 153}}$	$8.43_{{\color[rgb]{0.5,0.5,0.5}\pm 0.36}}$
	AECP	$0.70_{{\color[rgb]{0.5,0.5,0.5}\pm 0.04}}$	$330_{{\color[rgb]{0.5,0.5,0.5}\pm 76}}$	$8.24_{{\color[rgb]{0.5,0.5,0.5}\pm 0.51}}$	$0.68_{{\color[rgb]{0.5,0.5,0.5}\pm 0.03}}$	$775_{{\color[rgb]{0.5,0.5,0.5}\pm 134}}$	$10.09_{{\color[rgb]{0.5,0.5,0.5}\pm 1.09}}$	$0.44_{{\color[rgb]{0.5,0.5,0.5}\pm 0.03}}$	$871_{{\color[rgb]{0.5,0.5,0.5}\pm 112}}$	$6.72_{{\color[rgb]{0.5,0.5,0.5}\pm 1.24}}$
Opus -4.6	Single	$0.59_{{\color[rgb]{0.5,0.5,0.5}\pm 0.04}}$	$329_{{\color[rgb]{0.5,0.5,0.5}\pm 61}}$	$3.29_{{\color[rgb]{0.5,0.5,0.5}\pm 0.18}}$	$0.62_{{\color[rgb]{0.5,0.5,0.5}\pm 0.05}}$	$1{,}511_{{\color[rgb]{0.5,0.5,0.5}\pm 86}}$	$6.43_{{\color[rgb]{0.5,0.5,0.5}\pm 0.28}}$	$0.31_{{\color[rgb]{0.5,0.5,0.5}\pm 0.05}}$	$1{,}722_{{\color[rgb]{0.5,0.5,0.5}\pm 93}}$	$4.12_{{\color[rgb]{0.5,0.5,0.5}\pm 0.21}}$

Table 1: Main results across five models and three repository-construction benchmarks. Values report mean

\pm

standard deviation over three independent runs, where each run’s value is averaged over the benchmark’s tasks. Higher scores are better; lower time and token usage are better.

Method	AWS Strands			mini-SWE
Method	Score	Time	Token	Score	Time	Token
Single	$0.65$	$868$	$7.49$	$0.65$	$990$	$12.18$
Team-Freedom	$0.51$	$1{,}194$	$6.90$	$0.54$	$1{,}646$	$13.66$
Team-Integrator	$0.66$	$1{,}753$	$10.45$	$0.66$	$2{,}614$	$15.36$
AECP	$0.68$	$775$	$10.09$	$0.69$	$1{,}056$	$15.32$

Table 2: Cross-scaffold results on NL2Repo. Time in seconds; tokens in millions.

Figure 5: Efficiency gains and task difficulty. (a) Distribution of per-task orchestrator-token usage for Team-Integrator and AECP. (b) Team-token usage as Knowledge Sharing and then Contract Sharing are added to Team-Integrator . Action-category shares are normalized to the original Team-Integrator token total. (c) Wall time, total output tokens, and score by task difficulty for a single run of Opus-4.6 on NL2Repo.

Adaptive subset	Selected $N$		Forced $N=3$
Adaptive subset	Time (s)	Score	Time (s)	Score
$N=1$ (39 tasks)	$403.2$	$0.82$	$748.6$	$0.82$
$N=2$ (17 tasks)	$572.9$	$0.66$	$629.2$	$0.63$
$N=3$ (45 tasks)	$849.6$	$0.54$	$867.2$	$0.54$
$N>3$ (3 tasks)	$926.8$	$0.52$	$931.4$	$0.52$
All 104 tasks	$639.2$	$0.66$	$785.7$	$0.66$

Table 3: Matched adaptive versus fixed team sizing on 104 tasks. Each row compares the same task subset using the selected

N

and forced

N=3

Metric	Team-Freedom	AECP
Reach	$19/20$ ( $95\%$ )	$0/20$ ( $0\%$ )
Adoption	$8/20$ ( $40\%$ )	$0/20$ ( $0\%$ )

Table 4: Propagation of malicious instructions from a compromised worker across 20 attack cases.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6: From (a) a single general-purpose agent that uses tools and task-specific sub-agents to (b) an agent team composed of multiple such agents. Each teammate retains the capabilities shown in (a), while maintaining its own execution trajectory and coordinating changes to a common project.

Construct	Treatment
Default values	Parameter names, order, and keyword-only parameters are compared; default values are not.
args / *kwargs	Abstain on the call shape.
Missing annotations	Abstain on the type comparison.
Function decorators	The signature is read from the definition as written.
@overload	The final, implementing definition is checked.
@dataclass without init	The constructor is derived from the annotated fields.

Appendix

Table 5: Treatment of Python constructs by the static contract checker.

Method	w/o Adaptive Thinking			w/ Adaptive Thinking
Method	Score	Time (s)	Token (M)	Score	Time (s)	Token (M)	AT Rate
Single	$0.65$	$868$	$7.49$	$0.67$	$885$	$7.80$	$47\%$
Team-Freedom	$0.51$	$1{,}194$	$6.90$	$0.55$	$1{,}528$	$10.70$	$51\%$
Team-Integrator	$0.66$	$1{,}753$	$10.45$	$0.67$	$2{,}103$	$12.64$	$43\%$
AECP	$0.68$	$775$	$10.09$	$0.69$	$961$	$14.32$	$45\%$

Appendix

Table 6: Effect of adaptive thinking on Opus-4.8 performance on NL2Repo. Adaptive-thinking rates are reported as percentages.

Leader	Worker	Score	Time (s)	Cost ($/repo)
DS-V4-Flash	DS-V4-Flash	$0.425$	$1{,}180$	$\$ 0.12$
Opus-4.8	DS-V4-Flash	$0.502$	$1{,}287$	$\$ 0.69$
Opus-4.8	Opus-4.8	$0.680$	$775$	$\$ 2.03$

Appendix

Table 7: Leader–worker model allocation on NL2Repo.

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