cs.AIOct 19, 2026

Agentic Orchestration of EDC Data Space Retrieval and Natural Language Driven Data Processing

Authors: Marla Grunewald, Fin Gentzen, Iulisloi Zacarias, Admela Jukan

Organizations: Institute of Computer and Network Engineering, Technische Universität Braunschweig, Germany

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Abstract

Participating in a data space still requires the Dataspace Protocol (DSP) negotiation flow to be hand-coded against each provider's access policy. We present a multi-agent architecture that automates this process using natural language requests. Built on LangGraph over an Eclipse Dataspace Components (EDC), the design separates concerns into cooperating agents. The orchestrator's role is to route and plan. The data extraction agent's role is to expose the DSP protocol's three composable tools (catalog discovery, contract negotiation and download). The agent also gates negotiations on the consumers' verifiable credentials, refusing assets rather than fabricating them. Furthermore, there are downstream Machine Learning (ML) and Visualization agents, as well as a final Verification agent, which confirms that the request was actually fulfilled and rechecks the intended operation against the contract's usage policy. In a primary benchmark involving 600 runs, the system achieved 89% task success with zero policy violations when evaluating multi-dimensional policy reasoning with a locally hosted open-weight model. It failed safely through refusal rather than misrouting. The reliability of the system is largely dependent on the phrasing of requests: technical and expert requests succeed up to 98% of the time, whereas casual and under-specified requests succeed 72% of the time. Furthermore, in a secondary evaluation of 600 runs isolating hierarchical geographic access control, the framework successfully blocks 100% of unauthorized regional requests. While the verification agent hallucinates that 1% of these requests, the system logs confirm that no data is ever extracted and the framework simultaneously achieves an end-to-end verified delivery rate of 86% for eligible consumers.

Figures & tables

Figure 1: IDS reference Architecture for data space connectors as defined in [ 8 ] .
Figure 2: The proposed system architecture consists of the LangGraph Agentic System and the MVD . While the purple agents are mandatory for every call, the system requires at least one, and can utilize up to all three, of the green worker agents. In order to test the setup, we have added a file with user responses in order to automate the testing of the setup, this file is visible next to the TUI in the architecture.
 Policy Dimension  
 Geographic Allowed UsageEntity Profile 
ProviderEUGermanyLower SaxonyMonetaryTIVPPRICO(Closed)
Provier 1 ( P1P_{1} )✓✓✓€ 0.00✓✓✓✓✓ ✓
Provier 2 ( P2P_{2} ) ✓✓€ 500.00 ✓✓ ✓✓✓
Provier 3 ( P3P_{3} )  ✓€ 2000.00  ✓✓✓✓✗
Allowed Usage: Training (T), Inference (I), Visualization (V)
Table 1: Simulated ODRL Policy Constraints Across MVD Providers
Figure 3: The heatmap shows the success rate of the TC by Prompting style in %.
Figure 4: The Figure shows the correct and incorrect routing decisions for the Data Extraction Agent. Green cells are correct decisions, red cells are errors.
Figure 5: Failure analysis, where task failed and how often. The overall number of tasks is 600 runs.
Figure 6: The figure shows the latency by Agent Level in seconds in a boxplot where the whiskers are located at 1.5xIQR.
Figure 7: Gating Matrix of the tested geographic regions and Provider policies. Consumer locations are assigned in a 5-step round-robin cycle across the 600 runs (120 runs per location). Because each test case spans 12 consecutive runs (4 levels ×\times 3 styles) and 12 is not divisible by 5, each block allocates 3 runs to two locations and 2 runs to the other three, shifting by 12 mod 5=212\bmod 5=2 positions per test case. Since every 60-run replication restarts at the same phase, this split scales directly across 10 repetitions into the 20- and 30-run cell counts observed in the matrix.
Figure 8: Results of executions regarding geographic policy satisfaction or blocking. The top bar represents requests where the consumer location matches the provider restrictions. The middle bar represents requests where the consumer location does not match the provider restrictions; therefore, the multi-agent system should stop the negotiation process. The bottom bar represents requests with no eligible provider; thus, the request should be ignored or blocked.
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