Enterprise organizations are struggling to move AI agents from demos to production due to a lack of trust, missing audit trails, and no kill-switch controls. The core argument is that governance must be enforced by infrastructure agents cannot access or modify. Redpanda's Agentic Data Plane is presented as a centralized governance layer that sits between agents and all data/tools/models, providing verified agent identities, tamper-proof audit logs, least-privilege access scoping, per-agent spend caps, and a unified governance dashboard. The post contrasts this approach with bolt-on governance tools and vendor-locked AI platforms, emphasizing open architecture and governance by default.
Table of contents
Why are organizations struggling to deploy agentic systems? #How can organizations trust agents with their data? #Introducing the Redpanda Agentic Data Plane #What makes the Redpanda Agentic Data Plane different? #Watch the full Tech Talk for more #174 Impressions