[2605.29082] The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Plane

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Computer Science > Artificial Intelligence

arXiv:2605.29082 (cs)

[Submitted on 27 May 2026]

Title:The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Plane

Authors:Tyler Akidau, Tyler Rockwood, Johannes Brüderl, Marc Millstone<br>View a PDF of the paper titled The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Plane, by Tyler Akidau and 3 other authors

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Abstract:AI agents are increasingly expected to operate as digital employees: accessing enterprise data, making decisions, and taking actions autonomously. But agents are simultaneously less predictable than humans -- prone to hallucination, misinterpretation, and adversarial manipulation -- and more technically capable: with deep system knowledge and high-throughput interfaces cascading damage at machine speed. This combination makes it unsafe to rely on agents to faithfully interpret or propagate security-critical metadata such as access policies, data classifications, and behavioral constraints.

We present the Redpanda Agentic Data Plane (ADP), an architecture built around out-of-band metadata channels: infrastructure pathways that carry security context, policy signals, and audit trails deterministically, entirely outside the agent's read and write path and across heterogeneous infrastructure. These channels enforce governance at every stage of the agent lifecycle -- scoping data access on the way in, constraining actions during execution, and capturing tamper-proof transcripts on the way out.

We demonstrate ADP with a multi-agent portfolio rebalancing system in which autonomous agents monitor markets, make trade decisions, and execute orders across isolated client accounts -- with per-client data scoping, trade approval thresholds, and tamper-proof audit trails all enforced by out-of-band channels the agents can neither see nor bypass.

Comments:<br>6 pages, 1 figure. Published at SAO '26 (co-located with ACM CAIS '26)

Subjects:

Artificial Intelligence (cs.AI)

ACM classes:<br>K.6.5; I.2.11

Cite as:<br>arXiv:2605.29082 [cs.AI]

(or<br>arXiv:2605.29082v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.29082

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Tyler Akidau [view email]<br>[v1]<br>Wed, 27 May 2026 20:37:27 UTC (60 KB)

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