AI Agents Meet Real‑Time Data – Bridging the Gap

Meet Riya, a software engineer at a growing tech startup. A few months ago, Riya built an AI support chatbot to handle customer queries. This AI agent quickly became a hero of the support team – it answered FAQs, helped troubleshoot issues, and learned from each interaction. Encouraged by its success, her team created more AI agents for other tasks: one to triage bug reports, another to analyze user feedback, and even an agent to monitor system logs for anomalies. Each agent was impressive on its own, perceiving, reasoning, and acting within its niche. Everything seemed great… at first.

But as these agents rolled out, Riya noticed something troubling. Each agent was operating in a silo. The support bot had no clue what the log monitoring agent discovered, and the feedback analyzer worked in a vacuum separate from the bug triage agent. The team had built powerful AI assistants, yet they weren’t talking to each other. Important information was getting stuck within one agent and never reaching the others. In short, the company’s AI landscape was becoming a patchwork of isolated intelligences. Riya realized they were facing a growing problem: their AI agents lacked a shared context and any way to coordinate.

Islands of Automation: When One Agent Isn’t Enough

Riya’s experience is increasingly common. It started with one helpful agent, but soon there was a fleet of specialized agents each handling a slice of the workload. The pattern played out across her organization:

• The Customer Support Agent handled tickets but didn’t share data with anything else.
• The Sales Assistant Agent tracked leads in the CRM, unaware of insights from the analytics tools.
• The DevOps Agent watching system metrics operated completely independently.

At first, this agent per task approach seemed logical – each AI was tuned for a specific job. However, the more agents they deployed, the more apparent their fragmentation became. Riya jokingly referred to them as “islands of automation.” Each agent had its own database of knowledge and none had a boat to reach the others. The result was duplicated efforts and missed opportunities. For example, the sales team’s agent diligently summarized a customer’s past interactions, but it never knew that the data analysis agent had identified a new trend about that customer’s behavior. Valuable insights slipped through the cracks because the agents had no way to share information​.

Agent fragmentation soon began to hurt productivity. Riya’s team found themselves manually stitching together outputs: copying answers from one agent’s report into another’s input. It felt ironic – they introduced AI to automate tasks, yet now spent more time playing the intermediary between AIs. This fragmented setup led to obvious inefficiencies: two agents would sometimes research the same customer data separately, and the development team had to update each agent with context one at a time. Clearly, having multiple smart agents wasn’t very smart when they were deaf to each other.

When Agents Don’t Talk: The Cost of Isolation

As weeks went by, the lack of coordination between AI agents started causing real problems. One morning, Riya discovered that the customer support chatbot had recommended an outdated workaround to a user – something the internal system-monitoring agent already knew was no longer needed after a recent fix. Since the support agent and the monitoring agent never exchanged notes, the chatbot was operating with stale context. This contextual isolation meant each agent was making decisions with incomplete information. In another case, two different agents ended up contacting the same customer from two angles – one about an upsell opportunity and another about a support issue – creating a confusing user experience. If only the sales AI and support AI had been aware of each other’s activities, they could have coordinated a single, informed response instead of two conflicting ones.

Riya’s team identified several symptoms of their AI silos:

• Redundant work: Agents often fetched or computed similar data separately, duplicating effort.
  
  
• Missing insights: An insight generated by one agent stayed with that agent – others never knew about it, leading to decisions made without the full picture.
  
  
• Inconsistent actions: Without a common understanding, agents sometimes gave contradictory answers or took misaligned actions for the same customer.
  
  
• Hard to trace or audit at scale: Each agent maintained its own opaque history, making it painful to reproduce decisions, satisfy compliance reviews, or debug issues across the fleet.
  
  
• Rigid growth path: On‑boarding a new agent—or even upgrading an existing one—required point‑to‑point integrations, slowing innovation and limiting the team’s ability to experiment.

It became clear that even the most sophisticated AI models are constrained by their isolation from data and from each other​. Every new AI capability they added required custom wiring to connect it with the rest, and those ad-hoc integrations were becoming unmanageable. Riya likened it to the bad old days of applications not talking to each other – the dreaded data silos – except now the silos were intelligent agents. Instead of a connected AI ecosystem, they had a fragmented one, and it was difficult to scale or maintain. The promise of AI was to streamline work, but without fixing these silos, it was introducing new friction.

Riya knew something had to change. The team briefly considered consolidating into a single uber-agent, but that wasn’t practical – each agent existed for a reason, with different expertise and tools. They also tried quick fixes, like having one agent call another agent’s API. This helped a bit (for example, the support agent would ping the analytics agent for recent stats), but it was a brittle solution. Hard-coding one-to-one interactions between agents felt like playing whack-a-mole; every time they added a new connection, two more needs for integration popped up. With three or four agents, writing custom API calls between each pair was just about manageable. But as the number of agents grew, the point-to-point wiring became a tangle (as the illustration above shows). They needed a more elegant, scalable way for all these agents to stay in sync.

A Page from Microservices: One Event Bus to Rule Them All

One afternoon, during a brainstorming session, Riya had an epiphany. Years ago, she had helped transition part of the company’s software architecture from a monolithic design to microservices. In doing so, they faced a similar challenge of isolated services needing to communicate. The solution back then was to introduce a shared event bus – a real-time data stream where services publish events and subscribe to those they care about. That design pattern broke down silos in the software, allowing loosely coupled components to react to each other without hard-coded integrations. Riya realized the same principle could apply to their AI agents. Why not give the agents a “communication bus” to talk to each other?

The idea was simple: an event-driven pipeline where each AI agent could post updates about what it learns or does, and listen for relevant updates from others. Instead of direct agent-to-agent calls, they would all speak a common language of streaming events. For example, the support agent could emit an event like “UserIssueResolved” or “FeatureRequestReceived” onto the bus. The product feedback agent, subscribed to such events, would see the feature request and update its analysis. Meanwhile, the sales agent subscribed to “FeatureRequestReceived” might flag that user as a potential beta tester for a new feature. In return, the sales agent might publish an event “CustomerUpsellOpportunity,” which the support agent could pick up to know that this user is being targeted for an upsell – thus the next support interaction can be tailored. All of this coordination could happen asynchronously, in real-time, through a shared data streaming platform rather than explicit APIs.

Riya’s team was excited about this concept. Essentially, they were planning to give their AI agents a shared brain — not by merging them into one AI, but by creating a shared memory space and message pipeline. The agents would remain independent entities (each with its own role and model), but they would no longer be context-isolated. The event bus would act as the universal translator and meeting place for their AI ecosystem. It’s like building bridges between islands so information can flow instantly rather than  waiting for a ship to carry a message across the water.

Building a Shared Brain: Real-Time Data as Common Context

Once the team decided on a shared event bus approach, implementation began. They set up a real-time data streaming system (Apache Pulsar, Apache Kafka, or StreamNative Ursa) to serve as the central highway for events. Each agent was instrumented to do two things: publish events about any noteworthy action or finding it had, and listen for events from others that it might care about. This design immediately paid off. Now, when the data analytics agent detects a trend or anomaly, it publishes an event (TrendDetected) to the bus. The sales and support agents subscribe to such events and automatically enrich their own responses with that info – no more blind spots. Likewise, the support bot emits a MajorIssueOpened event when a high-priority ticket is created; the other agents all get the memo in real time. Suddenly, the isolated islands became an archipelago with bridges between them.

Crucially, this shared data layer serves as a single source of truth and context for all agents. It’s as if all the agents now speak a common language. The nasty tangle of point-to-point integrations was replaced by each agent integrating just once – to the event stream. Riya no longer had to write special-case code for the support agent to query the analytics agent or vice versa; the publish/subscribe model handled it gracefully. This not only made the system simpler, but also more extensible. When they decided to add a new AI agent (for example, a Knowledge Base Agent to automatically draft documentation from support tickets), they simply plugged it into the event bus. Immediately, it could start consuming events like UserQuestionAsked or BugResolved and contribute by posting its own events (ArticleDraftCreated). No complex integration work was required to connect this new agent with every other service – one connection unlocked all the others.

The transformation was remarkable. The AI agents, once fragmented, were now coordinating like a well-trained team. They exchanged facts and findings in real time, leading to outcomes that none of the individual agents could achieve alone. The support agent’s answers became more context-aware (since it “knew” about latest product updates and user activity), the sales agent picked much better timing to reach out to customers, and the engineering team gained a collective view of what all the AIs were learning. Riya’s narrative had shifted from isolated intelligence to integrated intelligence. By introducing a shared real-time event bus, the team had effectively bridged the gap between their AI agents and created a unified, collaborative system.

From Isolation to Intelligence: The Path Forward

Riya’s journey illustrates a key insight in the era of AI: to unlock the full potential of AI agents, we must connect them through shared data and context. It’s not enough to deploy dozens of isolated agents, no matter how advanced each one is. Without a shared “language” or data fabric, we end up with AI silos and lost opportunities. The solution is to treat real-time data as a common substrate that all agents can draw from and contribute to. In essence, the organization needs a nervous system for AI – a way for each agent (the “neurons”) to fire signals that the others can sense and respond to.

Forward-looking teams are already embracing this approach. Some are implementing open standards for agent communication so that different AI systems can interoperate on a common bus. (For instance, Anthropic’s introduction of a Model Context Protocol proposes a unified way for AI tools to exchange context, highlighting industry recognition of this need​.) The exact technology can vary – whether it’s built on message queues, data streaming platforms, or specialized agent coordination frameworks – but the core idea is consistent: real-time data sharing to break down AI silos. Just as APIs once allowed disparate software services to work together, this real-time event bus gives AI agents a medium to collaborate.

Instead of each new agent requiring a custom integration to understand the world around it, teams can “plug and play” their agents into a central stream of events and facts. This dramatically lowers the effort to add or upgrade AI capabilities. It also adds robustness – if one agent goes down or is replaced, others continue to communicate via the bus with minimal disruption. The overall system becomes more adaptive and intelligent as it can recombine the skills of multiple agents on the fly. In short, a shared real-time event bus turns a collection of smart but isolated agents into a coordinated, collectively smarter whole.

Bridging the Gap – Your Next Steps 🚀

Riya’s story is becoming the new reality for many developers integrating AI into their apps. We, as developers and tech leaders, have a chance to shape this emerging paradigm of connected AI agents. The vision is clear: bring real-time data streams and AI agents together to create an agile, intelligent ecosystem. It’s time to move beyond deploying AI agents in isolation and towards building systems where they can truly work in concert.

This post is only the starting point. Over the next few weeks, we’ll publish additional posts in our new Agentic AI blog series, exploring how real‑time data, open standards, and modern runtime design come together to enable your AI agents to “talk” to your data and to each other in real time.  If you’d like a first look at what’s next, join us at the Data Streaming Summit 2025 on May 28 - 29, where we'll showcase these ideas in action..

Don’t let your AI agents live on islands. By connecting them with a real-time data backbone, you empower them to collaborate, adapt, and achieve far more together than they ever could alone. The era of Agentic AI is just beginning—let’s build a fleet of data‑driven agents together.

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