Enterprise AI brokers in the present day face a elementary timing downside: They will't simply act on essential enterprise occasions as a result of they aren't all the time conscious of them in real-time.
The problem is infrastructure. Most enterprise information lives in databases fed by extract-transform-load (ETL) jobs that run hourly or each day — finally too gradual for brokers that should reply in actual time.
One potential strategy to deal with that problem is to have brokers immediately interface with streaming information techniques. Among the many main approaches in use in the present day are the open supply Apache Kafka and Apache Flink applied sciences. There are a number of business implementations based mostly on these applied sciences, too, Confluent, which is led by the unique creators behind Kafka, being certainly one of them.
Right now, Confluent is introducing a real-time context engine designed to unravel this latency downside. The know-how builds on Apache Kafka, the distributed occasion streaming platform that captures information as occasions happen, and open-source Apache Flink, the stream processing engine that transforms these occasions in actual time.
The corporate can also be releasing an open-source framework, Flink Brokers, developed in collaboration with Alibaba Cloud, LinkedIn and Ververica. The framework brings event-driven AI agent capabilities on to Apache Flink, permitting organizations to construct brokers that monitor information streams and set off robotically based mostly on circumstances with out committing to Confluent's managed platform.
"Right now, most enterprise AI techniques can't reply robotically to essential occasions in a enterprise with out somebody prompting them first," Sean Falconer, Confluent's head of AI, informed VentureBeat. "This results in misplaced income, sad prospects or added threat when a fee fails or a community malfunctions."
The importance extends past Confluent's particular merchandise. The trade is recognizing that AI brokers require totally different information infrastructure than conventional functions. Brokers don't simply retrieve info when requested. They should observe steady streams of enterprise occasions and act robotically when circumstances warrant. This requires streaming structure, not batch pipelines.
Batch versus streaming: Why RAG alone isn't sufficient
To grasp the issue, it's essential to differentiate between the totally different approaches to transferring information via enterprise techniques and the way they’ll connect with agentic AI.
In batch processing, information accumulates in supply techniques till a scheduled job runs. That job extracts the information, transforms it and masses it right into a goal database or information warehouse. This would possibly happen hourly, each day and even weekly. The method works effectively for analytical workloads, however it creates latency between when one thing occurs within the enterprise and when techniques can act on it.
Knowledge streaming inverts this mannequin. As an alternative of ready for scheduled jobs, streaming platforms like Apache Kafka seize occasions as they happen. Every database replace, person motion, transaction or sensor studying turns into an occasion printed to a stream. Apache Flink then processes these streams to affix, filter and mixture information in actual time. The result’s processed information that displays the present state of the enterprise, updating constantly as new occasions arrive.
This distinction turns into essential when you think about what sorts of context AI brokers really want. A lot of the present enterprise AI dialogue focuses on retrieval-augmented era (RAG), which handles semantic search over information bases to seek out related documentation, insurance policies or historic info. RAG works effectively for questions like "What's our refund coverage?" the place the reply exists in static paperwork.
However many enterprise use circumstances require what Falconer calls "structural context" — exact, up-to-date info from a number of operational techniques stitched collectively in actual time. Think about a job advice agent that requires person profile information from the HR database, shopping habits from the final hour, search queries from minutes in the past and present open positions throughout a number of techniques.
"The half that we're unlocking for companies is the flexibility to basically serve that structural context wanted to ship the freshest model," Falconer stated.
The MCP connection downside: Stale information and fragmented context
The problem isn't merely connecting AI to enterprise information. Mannequin Context Protocol (MCP), launched by Anthropic earlier this yr, already standardized how brokers entry information sources. The issue is what occurs after the connection is made.
In most enterprise architectures in the present day, AI brokers join by way of MCP to information lakes or warehouses fed by batch ETL pipelines. This creates two essential failures: The information is stale, reflecting yesterday's actuality moderately than present occasions, and it's fragmented throughout a number of techniques, requiring important preprocessing earlier than an agent can cause about it successfully.
The choice — placing MCP servers immediately in entrance of operational databases and APIs — creates totally different issues. These endpoints weren't designed for agent consumption, which might result in excessive token prices as brokers course of extreme uncooked information and a number of inference loops as they attempt to make sense of unstructured responses.
"Enterprises have the information, however it's usually stale, fragmented or locked in codecs that AI can't use successfully," Falconer defined. "The true-time context engine solves this by unifying information processing, reprocessing and serving, turning steady information streams into reside context for smarter, quicker and extra dependable AI choices."
The technical structure: Three layers for real-time agent context
Confluent's platform encompasses three parts that work collectively or adopted individually.
The real-time context engine is the managed information infrastructure layer on Confluent Cloud. Connectors pull information into Kafka matters as occasions happen. Flink jobs course of these streams into "derived datasets" — materialized views becoming a member of historic and real-time indicators. For buyer assist, this would possibly mix account historical past, present session habits and stock standing into one unified context object. The Engine exposes this via a managed MCP server.
Streaming brokers is Confluent's proprietary framework for constructing AI brokers that run natively on Flink. These brokers monitor information streams and set off robotically based mostly on circumstances — they don't look ahead to prompts. The framework contains simplified agent definitions, built-in observability and native Claude integration from Anthropic. It's out there in open preview on Confluent's platform.
Flink Brokers is the open-source framework developed with Alibaba Cloud, LinkedIn and Ververica. It brings event-driven agent capabilities on to Apache Flink, permitting organizations to construct streaming brokers with out committing to Confluent's managed platform. They deal with operational complexity themselves however keep away from vendor lock-in.
Competitors heats up for agent-ready information infrastructure
Confluent isn't alone in recognizing that AI brokers want totally different information infrastructure.
The day earlier than Confluent's announcement, rival Redpanda launched its personal Agentic Knowledge Airplane — combining streaming, SQL and governance particularly for AI brokers. Redpanda acquired Oxla's distributed SQL engine to offer brokers commonplace SQL endpoints for querying information in movement or at relaxation. The platform emphasizes MCP-aware connectivity, full observability of agent interactions and what it calls "agentic entry management" with fine-grained, short-lived tokens.
The architectural approaches differ. Confluent emphasizes stream processing with Flink to create derived datasets optimized for brokers. Redpanda emphasizes federated SQL querying throughout disparate sources. Each acknowledge brokers want real-time context with governance and observability.
Past direct streaming opponents, Databricks and Snowflake are basically analytical platforms including streaming capabilities. Their power is advanced queries over massive datasets, with streaming as an enhancement. Confluent and Redpanda invert this: Streaming is the muse, with analytical and AI workloads constructed on high of information in movement.
How streaming context works in apply
Among the many customers of Confluent's system is transportation vendor Busie. The corporate is constructing a contemporary working system for constitution bus firms that helps them handle quotes, journeys, funds and drivers in actual time.
"Knowledge streaming is what makes that potential," Louis Bookoff, Busie co-founder and CEO informed VentureBeat. "Utilizing Confluent, we transfer information immediately between totally different components of our system as an alternative of ready for in a single day updates or batch reviews. That retains all the pieces in sync and helps us ship new options quicker.
Bookoff famous that the identical basis is what is going to make gen AI beneficial for his prospects.
"In our case, each motion like a quote despatched or a driver assigned turns into an occasion that streams via the system instantly," Bookoff stated. "That reside feed of data is what is going to let our AI instruments reply in actual time with low latency moderately than simply summarize what already occurred."
The problem, nevertheless, is the best way to perceive context. When 1000’s of reside occasions movement via the system each minute, AI fashions want related, correct information with out getting overwhelmed.
"If the information isn't grounded in what is going on in the true world, AI can simply make fallacious assumptions and in flip take fallacious actions," Bookoff stated. "Stream processing solves that by constantly validating and reconciling reside information in opposition to exercise in Busie."
What this implies for enterprise AI technique
Streaming context structure indicators a elementary shift in how AI brokers devour enterprise information.
AI brokers require steady context that blends historic understanding with real-time consciousness — they should know what occurred, what's occurring and what would possibly occur subsequent, all of sudden.
For enterprises evaluating this method, begin by figuring out use circumstances the place information staleness breaks the agent. Fraud detection, anomaly investigation and real-time buyer intervention fail with batch pipelines that refresh hourly or each day. In case your brokers have to act on occasions inside seconds or minutes of them occurring, streaming context turns into needed moderately than optionally available.
"While you're constructing functions on high of basis fashions, as a result of they're inherently probabilistic, you employ information and context to steer the mannequin in a course the place you need to get some type of final result," Falconer stated. "The higher you are able to do that, the extra dependable and higher the end result."
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