🤖 AI Summary
Enterprise-grade autonomous agents struggle to reliably handle security-critical metadata—such as access policies and data classifications—due to hallucinations, misinterpretations, or adversarial attacks, thereby introducing systemic risks. This work proposes Redpanda Autonomous Data Plane (ADP), which pioneers a paradigm shift by completely removing security metadata from the agent’s operational path. ADP enforces policy compliance and tamper-proof auditing through infrastructure-level out-of-band channels that transmit security context, policy signals, and audit logs invisibly and non-bypassably. Evaluated in a multi-agent portfolio rebalancing system, ADP successfully ensured strict customer data isolation, enforced transaction approval thresholds, and maintained comprehensive auditability, demonstrating its efficacy and robustness in real-world deployments.
📝 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.