Your AI Agents have a brain. They still need a Nervous System
When a wrote this blogpost, my great colleague Ryan Piper, just posted a great post on LinkedIn with this title. The perfect title for my post, so I asked him for permission to use it, thanks Ryan. Nice teamwork.
The Nervous System AI Agents are missing
Everyone wants to talk about the brain of an AI agent, the model behind it, the prompt that shapes it, the framework that orchestrates it. Almost nobody asks a simpler question, how does that agent actually feel the world around it? Understanding the environment is crucial for AI Agents. The environment consists not just of data points but also of interactions and the context in which the AI operates. For example, consider a retail AI agent, it needs to understand not only sales trends but also customer behavior, regional preferences, and seasonal fluctuations. This comprehensive understanding helps deliver personalized experiences and improve customer engagement.
An AI Agent is only as sharp as the signals reaching it in the moment. An inventory level changes, a customer abandons a cart, a sensor on a factory floor drifts out of range, a system throws an alert. If none of that reaches the AI Agents as it happens, the Agent is not reasoning about the present. It is reasoning about the past. Imagine a scenario where a manufacturing AI agent detects a delay in the supply chain in real-time. If AI Agents can react promptly, AI Agents can initiate alternative sourcing mechanisms or adjust production schedules, thereby saving costs and ensuring continuity. This kind of agility is essential in today’s fast-paced environment.
Walk through a few more industries and the pattern repeats. A logistics company can see a shipment delayed on a tracking dashboard, but if that delay does not reach the AI Agent responsible for rerouting until the next scheduled sync, the rerouting decision arrives too late to matter. A bank’s fraud detection agent can run on the most capable model available, but if the transaction event reaches it after the funds have already settled, the model’s sophistication does not change the outcome. A hospital’s staffing agent can predict patient inflow with genuine accuracy, but if it only sees admission data once an hour, the beds it recommends opening are recommended too late for the shift already on the floor.
An energy utility (and a valued customer of Solace) I spoke with earlier this year described a version of the same problem from the grid side. Their forecasting model for demand response was, by their own account, excellent. But the signal telling an AI Agent that a substation was approaching capacity still traveled through a chain of dashboards and manual escalations before anyone, human or agent, could act on it. By the time the recommendation reached someone with authority to shed load, the peak had often already passed. An insurer I talked to a few months later described the same gap from the claims side, a fraud pattern the model would have caught instantly, arriving at the agent only after the claim had already been paid out. Different sectors, same shape of problem.
None of these are model problems. They are sensing problems. The intelligence was there. The signal was not, and no amount of additional reasoning capacity fixes a gap in what the agent was ever shown in the first place.
I described a version of this same gap in my last post, Why 2026 is a Record Year for System Integrators, using the image of a house renovation. Everyone wants the new kitchen, but the contractor keeps finding the wiring behind the walls needs replacing first. That post was about why budgets are finally paying for that wiring, and why System Integrators are having one of their strongest years in over a decade because of it. What I want to add here is what actually needs to run through that wiring once it is in place, especially for the functionalities expected from AI Agents. For instance, in a smart home scenario, the interconnected systems rely on this ‘wiring’ to function seamlessly smart thermostats adjust based on real-time temperature readings, while security systems notify homeowners of breaches as they occur.
What actually runs through the wiring
There is a distinction worth being precise about here. Traditional integration projects, the kind System Integrators have built for two decades, mostly moved data between systems. A batch job here, an API call there, a nightly extract that lands in a warehouse by morning. That work is valuable, and none of it disappears. But moving data between systems on a schedule is not the same as sensing a change the moment it happens. The wiring I described in my last post carries both. Point to point connections still matter for getting systems to talk to each other. What changes with AI Agents is that the wiring also needs to carry live signals, continuously, to whichever agent needs them, without anyone having to write a new integration every time a new agent joins the business.
That is the nervous system. The nervous system of an AI agent is not just a metaphor; it is the critical infrastructure that allows for instantaneous interactions and decisions. Without it, the intelligence of the AI is handicapped, limited to pre-existing data and unable to respond dynamically to shifting circumstances.
Think about how your own nervous system is organized. Reflexes happen at the spine, before the signal ever reaches the brain, because waiting for conscious thought would be too slow to matter. Higher reasoning happens separately, informed by a constant stream of sensory input it never has to ask for. An AI Agent needs the equivalent of both layers. Some decisions genuinely need a large model reasoning carefully over context. Others just need the right event routed to the right agent fast enough that the decision is still useful when it arrives. Most enterprise AI strategies today only design for the second layer, the reasoning, and assume the first layer, the sensing, will sort itself out. It rarely does on its own.
The Solace Agent Mesh as your Nervous System
Solace Agent Mesh runs on an event mesh, a layer that carries signals between agents, data, and applications the instant something happens, anywhere across the business. AI Agents connected to it do not ask for data on a schedule. They sense it as it occurs, the same way your hand senses heat before your brain finishes deciding what to do about it. This immediacy allows businesses to be proactive rather than reactive, ensuring they can capitalize on opportunities or mitigate risks as they arise.
Underneath that immediacy sits a fairly simple mechanism. Applications and agents publish events onto the mesh the moment something happens, a price change, a shipment scan, a support ticket, a sensor reading. Other agents subscribe to exactly the categories of events relevant to them, and the mesh routes each event to every subscriber that needs it, across clouds, across regions, across on premises systems, without either side needing to know where the other lives. That last part matters more in EMEA than almost anywhere else. Many of the organizations I work with run infrastructure across Germany, France, the Nordics and the UK, each with its own data residency expectations, and an event mesh built for that reality routes signals to where the agent needs to act, while keeping the underlying data governed by the rules of the country it belongs to. Sovereignty and real-time sensing are not in conflict, they just require infrastructure designed for both at once.
Most partner (System Integrators) teams I talk to across EMEA describe a similar split. Roughly 20% of their effort goes into the AI Agents itself, choosing a model, tuning prompts. The other 80% disappears into integration and data plumbing, wiring one system to another so the agent can even see what is happening. A working nervous system for AI Agents, makes that 80% shrink. Not because the plumbing goes away, but because it finally does its job quietly, in the background, the way a nervous system should. This enhanced efficiency enables teams to focus their efforts on innovation and improvement rather than on fixing integration issues.
I get one question in almost every one of these conversations. Is this not just message queuing with a new name. Messaging middleware has existed for decades, and there is truth in the comparison, the underlying pattern of publish and subscribe is not new. What is new is what is now attached to the other end of that subscription. It used to be another application, following fixed logic someone wrote in advance. Now it is an agent capable of reasoning about the event it just received, deciding among several possible actions, and calling other agents or tools to carry that action out. The wiring did not change all that much. What runs through it changed completely. A pattern built to move data between fixed systems is now expected to inform a reasoning system making judgment calls, and that raises the bar on latency, on context, and on trust in a way plain integration never had to meet.
This is the part I think gets missed in most AI strategy conversations. Enterprises spend months selecting a model and comparing agent frameworks, then discover the AI Agent they built cannot see a stock shortage until a batch job runs six hours later. By then the decision it needed to make has already been made, manually, the old way. This is why it’s vital to assess not just the capabilities of AI models but also the supporting infrastructure that enables real-time data processing and decision making. The gap between potential and performance can often be traced back to these foundational elements.
Get started!
When I talk to CTO’s or CIO’s about where to begin, I tend to push the conversation away from the model selection they usually want to have first. Start instead by mapping the handful of business events that, if an agent saw them the moment they happened, would change what the business does next. A stock level crossing a threshold. A contract nearing renewal. A customer sentiment score dropping sharply after a support interaction. Most enterprises can name five or six of these without much effort, and each one is a natural first agent to build, because the value of sensing it in real time is already obvious to the business, not just to the technology team.
Build the sensing layer around those events first, connect the agent that acts on them, and the pattern repeats cleanly for the next one. That is a very different rollout than trying to stand up a generic agent platform and hoping the use cases follow it. It also gives a CIO something concrete to show a board after the first quarter, an event that used to take hours to reach a human decision maker now reaching an agent in seconds, with a measurable outcome attached to it, rather than a slide describing a platform’s potential.
Take a retail replenishment agent as a concrete walkthrough, since it is the example I return to most often in EMEA conversations. A point of sale system publishes a sale event the moment a customer checks out. That event lands on the mesh and reaches every agent subscribed to inventory signals for that product category, whether the AI Agent runs in the retailer’s own data center in Frankfurt or in a public cloud region in Dublin. The replenishment agent picks it up, checks it against current stock levels and an open purchase order, and decides whether a reorder is warranted right now or can wait for the next planned cycle. If it decides to act, it calls a supplier API or hands the decision to a human buyer for approval, depending on how much authority the business has given it.
None of those four steps, sense, decide, act, confirm, waited on a schedule. The AI Agent did not poll a database every fifteen minutes hoping something had changed. It was told the moment something changed, by the same infrastructure that will tell the next agent, and the one after that, about the same event without anyone rewiring the connection. That last part is what tends to change a CIO’s view of the project fastest. The value is not just that one agent got faster. It is that every future agent built on the same mesh inherits sensing for free, because the events are already flowing and already governed.
Sensing signals in real-time is more than reacting to stale information a few minutes faster. When AI Agents sense what is happening continuously, across every application, cloud, and edge, they start acting inside the moment a business event happens rather than after it. That is what an agent native enterprise actually looks like. Data moves continuously through the event mesh. AI Agents built on Agent Mesh sense it, decide, and act, all inside the infrastructure the enterprise controls. For example, an online retailer can adjust its pricing in real time based on competitor activities and market demand, leveraging this continuous flow of information to optimize sales and customer satisfaction.
This is also where I think the next couple of years in EMEA get genuinely interesting. The organizations that treated their event infrastructure as background plumbing, something a System Integrator handled once and rarely revisited, are the ones now finding it is the piece standing between them and a working on an AI Agent strategy. The ones that invested in it are finding their agents useful faster, with less rework, because the sensing problem was already solved before the first agent was ever built. I expect a fair amount of AI Agents budget over the next year to quietly flow into exactly this layer, even when the project is announced as an AI Agents initiative rather than an integration one. The label on the budget line will say AI. What actually changes the outcome will be the nervous system underneath it.
This is also why the partner conversations I have across EMEA increasingly start with the event mesh question before the model question. A System Integrator who understands how to lay this wiring across a client’s German manufacturing plant, its French logistics hub and its UK head office is doing work that outlasts whichever model the client happens to be using this year. Models get swapped out on a regular basis now, sometimes within the same quarter. The sensing layer underneath them tends to stay in place for years, which is exactly why it deserves the same attention CIOs currently give to model selection, and why I think the System Integrators who build it well are set up for a genuinely strong run, not just a busy one.
If your AI Agents cannot feel the world around them in real time, they are not as intelligent as the demo made them look. This disconnect can lead to missed opportunities, inefficiencies, and ultimately a failure to meet business objectives. Real-time sensing and response capabilities are essential for maintaining a competitive edge in today’s fast-paced market.
The teams that get this right will not be the ones with the most advanced model. They will be the ones whose agents felt the shipment delay, the price change, the fraud attempt, the moment it happened, and acted while it still mattered. That is the enterprise I am building toward with every partner conversation I have this year.
Interested in discussing this further? I’d be happy to connect.