# Redpanda Redpanda is a Kafka® compatible, ZooKeeper®-free, JVM-free and source available streaming data platform. Use all your favorite open source tooling without any code changes. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Streamhouse Working Group proposes a vendor-neutral architecture for real-time data infrastructure DevFeed: [Streamhouse Working Group proposes a vendor-neutral architecture for real-time data infrastructure]() Original publisher: [Read original article]() Author: Alexander Gallego Published: 2026-09-15T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Data Infrastructure](), [real-time](), [Streaming](), [Artificial Intelligence](), [Redpanda-Connect]() Tags: [ai](), [announcement](), [data-infrastructure](), [real-time](), [redpanda-connect](), [streaming]() ### AI overview Redpanda says it has joined Aiven, Confluent, StreamNative, and Ververica to form the Streamhouse Working Group, which proposes a vendor-neutral architecture for real-time operational data infrastructure. The article distinguishes Streamhouse from the lakehouse by focusing on continuously processing current data for production applications and AI agents. ### Source excerpt Redpanda has joined Aiven, Confluent, StreamNative, and Ververica to form the Streamhouse Working Group. Here's what that means for the future of real-time data infrastructure. ## Redpanda named a Leader in G2's Fall 2026 Event Stream Processing Reports DevFeed: [Redpanda named a Leader in G2's Fall 2026 Event Stream Processing Reports]() Original publisher: [Read original article]() Author: Redpanda Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [stream-processing](), [streaming-data-processing](), [Usability](), [real-time](), [Streaming](), [Software]() Tags: [2026](), [announcement](), [event](), [performance](), [real-time](), [reports](), [software](), [stream-processing](), [streaming](), [usability]() ### AI overview Redpanda reports that it was recognized in 10 G2 Fall 2026 Event Stream Processing reports, earning first place in eight indexes, including implementation, usability, results, and relationship. The article also highlights Redpanda Shadowing for migration from Confluent and cites a 4.7 out of 5 average rating across 53 G2 reviews. ### Source excerpt Redpanda ranked #1 in Implementation, Usability, Results, and Relationship Indexes in G2's Fall 2026 Event Stream Processing Reports. ## CoreBreak proves agent guardrails need to live outside the agent DevFeed: [CoreBreak proves agent guardrails need to live outside the agent]() Original publisher: [Read original article]() Author: Tyler Akidau Published: 2026-08-31T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [AI Agent](), [Security](), [Securing AI](), [Amazon Web Services](), [vercel ai sdk](), [Amazon Bedrock](), [Google](), [Vercel]() Tags: [agent](), [agentic-ai](), [ai](), [ai-agent](), [architecture](), [aws](), [bedrock](), [cve](), [google](), [policy](), [security](), [thought-leadership](), [vercel]() ### AI overview The article argues that CoreBreak exposed a structural security flaw in major AI agent stacks and that agent guardrails should be enforced outside the agent's control. ### Source excerpt At Black Hat 2026, CoreBreak exposed the same structural flaw across AWS, Google, and Vercel. Here's why AI agent security enforcement should live beyond the agent's reach. ## Push-button migration from Confluent to Redpanda with Shadowing DevFeed: [Push-button migration from Confluent to Redpanda with Shadowing]() Original publisher: [Read original article]() Author: Trevor Blackford Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [migration](), [Replication](), [Kafka](), [Confluent Cloud](), [Confluent Platform](), [Disaster Recovery](), [Usability]() Tags: [big-bang](), [confluent-cloud](), [confluent-platform](), [data-centers](), [disaster-recovery](), [kafka](), [migration](), [replication](), [schema]() ### AI overview The article presents Redpanda Shadowing 26.2 as a low-risk migration path from Confluent Cloud, Confluent Platform, or other Apache Kafka-compatible clusters. It replicates topic data, schemas, offsets, and ACLs while preserving offsets, allowing teams to validate workloads and move applications individually instead of using a big-bang cutover. ### Source excerpt Migrate off Confluent without the "big cutover weekend." Redpanda Shadowing carries topic data, schemas, offsets, and ACLs on a single link. Available on Self-Managed , BYOC, and Dedicated. ## 4 FAQs about designing agentic systems for production DevFeed: [4 FAQs about designing agentic systems for production]() Original publisher: [Read original article]() Author: Kristin Crosier Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [ai-governance](), [Responsibility & Safety](), [Architecture & Design](), [observability]() Tags: [agentic](), [ai-agents](), [ai-governance](), [architecture](), [cheat-sheet](), [observability](), [security]() ### AI overview This article presents practical guidance for designing and operating enterprise AI agents in production. It focuses on governance, policy enforcement, data access controls, observability, immutable action logs, audit transcripts, emergency shutdown capabilities, and guardrails for token and budget usage. ### Source excerpt Learn about the technical challenges many organizations face while deploying agentic systems, and how to ensure AI agents can operate safely and consistently. ## Multi-region high availability for Kafka workloads with a single Stretch Cluster DevFeed: [Multi-region high availability for Kafka workloads with a single Stretch Cluster]() Original publisher: [Read original article]() Author: David Yu Published: 2026-08-11T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Kubernetes](), [Availability](), [Replication](), [Raft](), [Resilience](), [stream-processing]() Tags: [clusters](), [k8s](), [kafka](), [raft](), [release](), [replication](), [resilience](), [stream-processing]() ### AI overview Redpanda Operator 26.2 introduces generally available Stretch Clusters, allowing one logical Redpanda cluster to span multiple Kubernetes clusters and regions. The release provides synchronous replication with Raft-based automatic failover, safer broker rolling restarts, Redpanda Connect pipelines as Kubernetes resources, and Gateway API support for Redpanda Console. ### Source excerpt Redpanda Operator 26.2 brings GA Stretch Clusters for multi-region replication, Redpanda Connect pipelines as K8s resources, Gateway API support, and safer rolling restarts. ## Out-of-band Policy Engine: governance AI agents can't ignore DevFeed: [Out-of-band Policy Engine: governance AI agents can't ignore]() Original publisher: [Read original article]() Author: Tyler Akidau Published: 2026-08-03T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [shadow AI](), [Security](), [audit trail](), [Language models]() Tags: [agents](), [ai](), [ai-agents](), [announcement](), [audit-trail](), [governance](), [mcp](), [mcp-server](), [policy](), [security](), [shadow-ai]() ### AI overview The article argues that enterprises need out-of-band governance for AI agents because agents can create shadow AI and may alter safeguards or audit trails that they can access. It presents Redpanda's Out-of-Band Policy Engine and Agentic Data Plane as an approach in which policies remain outside the agent's reach. ### Source excerpt Somewhere in your company, right now, someone is building an agent. Here's how the latest release of Redpanda's Agentic Data Plane makes it safe to run them. ## Agentic kill switch is a database problem. So we built Redpanda SQL DevFeed: [Agentic kill switch is a database problem. So we built Redpanda SQL]() Original publisher: [Read original article]() Author: Alexander Gallego Published: 2026-08-03T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Databases](), [SQL](), [stream-processing](), [Google Cloud Platform (GCP)](), [Amazon Web Services](), [systems]() Tags: [agentic](), [announcement](), [aws](), [cloud](), [code](), [database](), [exploit](), [google-cloud](), [governance](), [models](), [olap](), [real-time](), [scale](), [sql](), [stream-processing](), [systems](), [tools]() ### AI overview The article presents Redpanda SQL as a database built for agentic governance. It describes real-time bridge queries, efficient SQL JOINs, a ring-buffer shuffle algorithm, and analytical processing designed to detect agent behavior and provide an agentic kill switch. Redpanda SQL is stated to be available on AWS and Google Cloud. ### Source excerpt Agentic governance needs a new kind of database, so we built Redpanda SQL. Now available on both AWS and Google Cloud. ## 10X more data, same 4 seconds: single-query scaling in Redpanda SQL on 1TB DevFeed: [10X more data, same 4 seconds: single-query scaling in Redpanda SQL on 1TB]() Original publisher: [Read original article]() Author: Marcin Grzebieluch Published: 2026-07-29T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [SQL](), [Benchmark](), [dataset](), [Apache Iceberg](), [Streaming]() Tags: [apache](), [benchmark](), [dataset](), [kafka](), [sql](), [streaming]() ### AI overview This article benchmarks how a single analytical query in Redpanda SQL scales as the dataset grows from 100 GB to 1 TB and as cluster resources increase. Redpanda SQL combines live-streaming topics with historical Apache Iceberg tables through bridge queries, and the benchmark uses skewed NYC Taxi trip data to evaluate strong scaling and query feasibility. ### Source excerpt A benchmark on how a single analytical query behaves in Redpanda SQL as the dataset and the cluster grow. ## Drag, drop, done: a visual composer for Redpanda Connect DevFeed: [Drag, drop, done: a visual composer for Redpanda Connect]() Original publisher: [Read original article]() Author: Prakhar Garg Published: 2026-07-23T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Redpanda-Connect](), [Canvas](), [stream-processing](), [Caching](), [Routing (disambiguation)](), [HTTP](), [n8n](), [workflow automation]() Tags: [api](), [automation](), [building](), [cache](), [caching](), [events](), [front-end](), [http](), [n8n](), [pipeline](), [redpanda-connect](), [routing](), [stream-processing](), [workflow]() ### AI overview Redpanda introduces a visual Pipeline Builder for Redpanda Connect, using a drag-and-drop canvas to compose pipelines from inputs, processors, and outputs. The builder generates YAML while making routing, enrichment, caching, error handling, and retries easier to understand and configure. The article contrasts it with n8n, emphasizing Redpanda Connect's suitability for high-volume event streaming with ordering, backpressure, and exactly-once semantics. ### Source excerpt A first look at the new visual Pipeline Builder for Redpanda Connect (preview), plus a roundup of recent CDC and connector updates. ## Deploy agents you can trust with centralized AI governance DevFeed: [Deploy agents you can trust with centralized AI governance]() Original publisher: [Read original article]() Author: Kristin Crosier Published: 2026-07-21T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [ai-governance](), [Responsibility & Safety](), [audit trail](), [SIEM, Security, Observability](), [Model Context Protocol](), [observability]() Tags: [agentic](), [ai](), [ai-governance](), [audit-trail](), [autonomous](), [autonomous-agents](), [enterprise](), [hosting](), [infrastructure](), [mcp](), [observability](), [thought-leadership](), [vibe-coding]() ### AI overview This article explains why enterprises struggle to deploy autonomous agentic systems: organizations lack confidence in agents that can access data and tools without unique identities, audit trails, or kill switches. It argues for a centralized AI governance layer enforced by infrastructure that agents cannot access or modify, positioned between agents and the data, tools, identities, and models they use. The proposed governance platform also supports scaling through AI gateways, MCP hosting, observability, and budgeting. ### Source excerpt Discover why organizations are struggling to deploy and scale agentic systems, and how a centralized AI governance platform can help you trust and scale agents. ## Full CDC semantics land in the Iceberg output for Redpanda Connect DevFeed: [Full CDC semantics land in the Iceberg output for Redpanda Connect]() Original publisher: [Read original article]() Author: Prakhar Garg Published: 2026-07-14T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Apache Iceberg](), [clickhouse](), [Database](), [Kafka]() Tags: [analytics](), [clickhouse](), [data](), [database](), [engineering](), [product](), [real-time](), [redpanda-connect](), [streams](), [sync]() ### AI overview This hands-on article explains how Redpanda Connect v4.99.0 applies full change data capture semantics to Apache Iceberg tables. Its output supports row-level inserts, upserts, and deletes, allowing a lakehouse table to stay synchronized with a source database in real time through a single pipeline definition. ### Source excerpt Full CDC semantics in Iceberg mean the lakehouse reflects what the source database looks like right now, not what it looked like during last night's batch window. Get to know the latest addition to Redpanda Connect. ## What is an Agentic Data Plane? DevFeed: [What is an Agentic Data Plane?]() Original publisher: [Read original article]() Author: Marc Millstone Published: 2026-07-09T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [agent observability](), [Amazon API Gateway](), [AWS IAM](), [observability](), [SIEM, Security](), [Amazon Web Services]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [api](), [api-gateway](), [aws](), [bedrock](), [enterprise](), [governance](), [iam](), [identity](), [identity-management](), [llm](), [models](), [observability](), [siem](), [thought-leadership](), [token](), [tool](), [tools]() ### AI overview The article defines an Agentic Data Plane as a governed governance and runtime layer between enterprise AI agents and the data, tools, identities, and models they access. It explains why existing IAM, API gateways, and observability or SIEM systems do not adequately govern agent-specific actions, identity propagation, model selection, token spending, policy enforcement, and end-to-end tracing. ### Source excerpt The Agentic Data Plane is the governance and runtime layer that connects your AI agents to everything they act on. Learn what it does, why existing tools can't replace it, and what to look for in an enterprise-grade one. ## Stream Oracle changes to ClickHouse in real time DevFeed: [Stream Oracle changes to ClickHouse in real time]() Original publisher: [Read original article]() Author: Prakhar Garg Published: 2026-07-07T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [clickhouse](), [Redpanda-Connect](), [Oracle Database](), [Kafka](), [real-time](), [data](), [Database](), [YAML]() Tags: [clickhouse](), [connectors](), [data](), [database](), [event](), [integration](), [kafka](), [oracle](), [pipeline](), [real-time](), [redpanda-connect](), [schema](), [yaml]() ### AI overview A hands-on walkthrough shows how to stream Oracle change data capture events through Redpanda Connect into ClickHouse in real time. It uses Oracle LogMiner and YAML-configured pipelines, with a Redpanda topic serving as an intermediate store before data is delivered to ClickHouse or other destinations. ### Source excerpt A hands-on walkthrough of the new Oracle input in Redpanda Connect. No Debezium, no Kafka Connect runtime, no JVM. ## Real-time Change Data Capture with Redpanda Connect and MySQL DevFeed: [Real-time Change Data Capture with Redpanda Connect and MySQL]() Original publisher: [Read original article]() Author: Chandler Mayo Published: 2026-07-02T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [MySQL](), [Kafka](), [Databases](), [Docker Compose](), [real-time](), [Docker](), [SQL]() Tags: [apache-kafka](), [connectors](), [container](), [docker](), [event](), [how-to](), [kafka](), [mysql](), [network](), [pipeline](), [real-time](), [sql](), [tutorial](), [volume]() ### AI overview A step-by-step tutorial for building a real-time change data capture pipeline with MySQL and Redpanda Connect. It explains how row-level inserts, updates, and deletes become downstream events without polling, using compatibility with the Kafka Connect ecosystem. ### Source excerpt A step-by-step tutorial on how to stream every insert, update, and delete from your database using MySQL and a faster, simpler alternative to Kafka Connect. ## How Redpanda Cloud Topics rethinks Kafka compaction DevFeed: [How Redpanda Cloud Topics rethinks Kafka compaction]() Original publisher: [Read original article]() Author: Willem Kaufmann Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [cloud-infrastructure](), [Cloud](), [Streaming](), [Algorithm]() Tags: [apache](), [apache-kafka](), [architecture](), [cloud-native](), [cloud-storage](), [clusters](), [cpu](), [cycles](), [engineering](), [kafka](), [object-storage](), [reduce](), [retention](), [scale](), [storage](), [streaming]() ### AI overview The article explains how Redpanda Cloud Topics redesigns Kafka log compaction for cloud-native streaming. It describes how the architecture reduces redundant processing and CPU use, lowers cloud storage costs, and preserves Kafka behavior while addressing scaling challenges such as limited memory, tombstone removal, and rewriting large volumes of object storage data. ### Source excerpt Compaction can overwhelm poorly sized Kafka clusters, leading to full disks and maxed-out CPUs. Learn how Redpanda's Cloud Topics architecture redesigns compaction to cut redundant work, reduce cloud storage costs, and preserve the Kafka semantics you rely on. ## Kafka's log compaction corrupts data. Here's how we fixed it DevFeed: [Kafka's log compaction corrupts data. Here's how we fixed it]() Original publisher: [Read original article]() Author: Alexey Bashtanov Published: 2026-06-25T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Apache-Kafka](), [bug](), [Streaming](), [Testing]() Tags: [apache-kafka](), [bug](), [engineering](), [kafka](), [streaming](), [testing]() ### AI overview The article examines a bug in Apache Kafka log compaction that can cause replicas to permanently disagree about retained records. It describes how tombstones and transaction control batches may be removed independently on different brokers, allowing deleted data to reappear or aborted data to be served as committed, and presents coordinated compaction in Redpanda Streaming as the solution. ### Source excerpt There's a problem with Apache Kafka's log compaction. Here's what we found, how to reproduce it, and how we solved it in Redpanda. ## Bridge Queries in Redpanda SQL DevFeed: [Bridge Queries in Redpanda SQL]() Original publisher: [Read original article]() Author: Paul Wilkinson Published: 2026-06-23T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Apache Iceberg](), [SQL](), [Streaming](), [parquet](), [Compression](), [real-time](), [data](), [dashboards]() Tags: [analytics](), [compression](), [data](), [engineering](), [parquet](), [real-time](), [sql](), [storage](), [streaming]() ### AI overview This article introduces bridge queries in Redpanda SQL, which combine historical data from Iceberg tables with recent messages from Redpanda topics through a single virtual SQL table. By reading the freshness gap directly from the topic, teams can flush data to Iceberg less frequently, producing better-sized Parquet files while retaining near-real-time query results and reducing compaction overhead. ### Source excerpt Stop choosing between fresh data and robust Parquet files. Redpanda SQL bridge queries let you query live streaming topics and historical Iceberg tables together, without the compaction overhead. ## Adaptive write request scheduling in Redpanda's Cloud Topics DevFeed: [Adaptive write request scheduling in Redpanda's Cloud Topics]() Original publisher: [Read original article]() Author: Evgeny Lazin Published: 2026-06-18T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Amazon S3](), [Latency](), [Cloud](), [data]() Tags: [batching](), [cloud](), [concurrent](), [cost](), [efficiency](), [engineering](), [kafka](), [latency](), [object-storage](), [s3]() ### AI overview This article explains Redpanda's adaptive write-request scheduler for Cloud Topics. Because each produce batch is uploaded to Amazon S3 before acknowledgment, the scheduler dynamically adjusts upload parallelism across CPU cores to balance batching efficiency, producer latency, throughput, and storage cost. It also describes how centralized batching avoids the latency and request-cost problems of running one batcher per shard. ### Source excerpt How we turned to the buddy allocator algorithm for Redpanda's Cloud Topics to balance batching efficiency against latency and cost. ## What's new in the Redpanda Agentic Data Plane DevFeed: [What's new in the Redpanda Agentic Data Plane]() Original publisher: [Read original article]() Author: Marc Millstone Published: 2026-06-16T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [Amazon Bedrock](), [Amazon Web Services (AWS)](), [AI Strategy](), [Model Context Protocol](), [observability](), [Cloud]() Tags: [agentic-ai](), [ai](), [announcement](), [aws](), [enterprise](), [governance](), [mcp](), [observability](), [platform](), [policy-management](), [production]() ### AI overview Redpanda announces the general availability of its Agentic Data Plane on AWS. The managed service unifies AI models, agents, and MCP-connected data sources with governance controls intended to help enterprises deploy agentic applications safely at scale. ### Source excerpt Redpanda Agentic Data Plane is now generally available on AWS. Deploy governed, enterprise-scale, agentic AI across all your data, safely. ## Cloud Topics: the Metastore DevFeed: [Cloud Topics: the Metastore]() Original publisher: [Read original article]() Author: Andrew Wong Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Cloud](), [rocksdb](), [data]() Tags: [architecture](), [cloud](), [consumer](), [engineering](), [kafka](), [learn](), [mapping](), [memory](), [object-storage](), [retention](), [rocksdb](), [scale](), [serialization](), [storage]() ### AI overview This article explains Redpanda's metastore for Cloud Topics. The metastore maps Apache Kafka offsets to byte ranges in L1 objects stored in object storage, while tracking metadata such as leader-term boundaries and compaction state. Redpanda built a general-purpose key-value store, inspired by LevelDB and RocksDB and implemented as an LSM tree, to scale metadata independently of memory, local disk, and metadata formats. ### Source excerpt Learn how Redpanda's metastore powers Cloud Topics, from offset lookups and whole cluster restore to cross-region read replicas, and why it's built to be a foundational primitive for the future. ## The four pillars for AI agent governance at scale DevFeed: [The four pillars for AI agent governance at scale]() Original publisher: [Read original article]() Author: Tyler Akidau Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [AI Agent](), [Authorization](), [ai security](), [Security](), [Artificial Intelligence](), [Securing AI](), [observability](), [prompt injection]() Tags: [ai](), [ai-agent](), [ai-security](), [authorization](), [enterprise](), [observability](), [prompt-injection](), [security](), [thought-leadership]() ### AI overview The article presents enterprise AI agent governance as an infrastructure problem rather than a model-quality problem. It identifies identity, authorization, observability, and accountability as four necessary pillars for deploying imperfect agents safely at scale, with controls enforced through infrastructure outside the agent's reach. ### Source excerpt AI agents need governance infrastructure, not just "better models". Here are the four pillars every enterprise needs to deploy agents safely at scale: identity, authorization, observability, and accountability. ## How OmniNode uses Redpanda to scale AI agent workflows DevFeed: [How OmniNode uses Redpanda to scale AI agent workflows]() Original publisher: [Read original article]() Author: Jonah Gray Published: 2026-06-02T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [AI-assisted coding](), [AI Agent](), [Software Engineering](), [pull-requests](), [Code]() Tags: [ai](), [ai-agent](), [engineering](), [pull-requests](), [software-engineering](), [writing-code]() ### AI overview OmniNode's founder describes using AI code generation and multiple agents to automate software engineering work beyond writing code, including pull-request review, failure tracing, configuration checks, and codebase-wide renames. The article explains that coordinating independent agents required routing tasks, tracking status, verifying results, and enforcing ordering. It also describes using Redpanda to scale an event catalog and maintain consistent topic names through contracts. ### Source excerpt OmniNode's founder shares his journey building the AI agent workflows that became OmniNode, and how Redpanda keeps topic names from drifting with contracts. ## Real-time streaming for the agentic era with NVIDIA DevFeed: [Real-time streaming for the agentic era with NVIDIA]() Original publisher: [Read original article]() Author: Melissa Czapiga Published: 2026-06-01T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Vera CPU](), [streaming-data-processing](), [Latency](), [Data Infrastructure](), [agent observability](), [systems]() Tags: [agentic](), [agents](), [announcement](), [compute](), [data-infrastructure](), [latency](), [nvidia](), [nvidia-vera](), [performance](), [streaming](), [systems]() ### AI overview Redpanda describes its collaboration with NVIDIA to run streaming workloads on NVIDIA Vera CPUs, reporting 5.5x lower latency for AI agents in mission-critical environments. The article discusses testing on workloads processing billions of messages daily, real-time data infrastructure, and potential benefits for enterprise AI and agentic applications. ### Source excerpt NVIDIA Vera launches today with Redpanda as part of the ecosystem, delivering 5.5x lower latencies for agents running in mission-critical environments. [Next page]()