How to Make Agentic AI Adaptive
with Dynamic Event Streams

The rise of agentic AI systems—autonomous agents that can perceive their environment, make decisions, and take actions—promises to transform how enterprises operate. But as these systems become more sophisticated, a critical challenge emerges: how do we give AI agents exactly the contextual information they need for their current goal, without drowning them in irrelevant data or compromising security? The answer lies in a principle that’s becoming increasingly clear: if context is king for getting accurate, relevant AI results, then enabling agents to flexibly and dynamically adjust real-time information access will be a key component of future AI systems.

The Context Challenge

Today’s AI agents face a fundamental dilemma. To make intelligent decisions, they need access to vast amounts of real-time information distributed across the enterprise—from IoT sensors and operational systems to customer databases and business applications. However, giving agents blanket access to all enterprise data creates unacceptable security and privacy risks.

But there’s an equally critical problem: information overload. If we constantly feed AI agents all available real-time information, we’ll overwhelm models that aren’t prepared for the relentless rush of data. Context windows fill up with irrelevant events. Processing costs skyrocket. Most importantly, the signal-to-noise ratio plummets—agents struggle to identify what matters for their current task amidst the flood of data.

Traditional request-response architectures fall short here. They require agents to know exactly what data to request and where to find it. But in dynamic operational environments, the relevant context is constantly shifting. An agent working on one problem suddenly needs different data streams when circumstances change.

EDA: The Foundation for Intelligent Agents

Event-driven architecture (EDA) offers a fundamentally better approach for agentic AI systems. Rather than agents pulling data from multiple sources, EDA allows agents to dynamically subscribe to precisely the information streams they need, when they need them.

The key advantage? Agents can adjust their information consumption in real-time based on their current tasks and context, without requiring pre-configured integrations to every possible data source.

That’s doubly true when using an event broker like Solace’s, which uses hierarchical smart topics to route events to interested agents. For more on that, check out this introduction to smart topics.

Real-World Example: Smart Bus Routing

Consider an AI agent responsible for optimizing bus routes in a metropolitan transit system. Under normal operations, the agent might subscribe to high-level summary events about fleet performance,  like a fleet status summary or critical route delays.

But when the agent detects an issue with Route 47—perhaps unusual delays or passenger complaints—it needs to dive deeper. Using an event-driven architecture utilizing an event broker like Solaces that supports Smart Topics, the agent can dynamically adjust its subscriptions to get granular, real-time telemetry about the bus’:

• Location
• Speed
• Passenger count
• Wait times
• Weather Conditions

All of this information can be persisted temporarily, potentially even in memory, giving the agent exactly the data it needs to query against to detect resolve the root cause of the issue. Once the issue is resolved, the agent can unsubscribe from the detailed telemetry, remove the persisted information,  and return to monitoring high-level metrics, reducing unnecessary data processing and costs.

The Security Imperative: Infrastructure-Level Safeguards

Dynamic information access is powerful, but it also raises serious security concerns. How do we prevent an agent from subscribing to sensitive topics it shouldn’t access? How do we ensure agents stay within their authorized boundaries?

This is where infrastructure-level safeguards become paramount. Rather than relying on the agent itself to respect boundaries, the event broker enforces security policies:

• Topic-Level Access Control: Each agent has a defined permission set that specifies exactly which topic hierarchies it can subscribe to. The transit routing agent might have access to transit information but be blocked from personal information about drivers or financial transactions.
• Dynamic Authorization: As agents adjust their subscriptions, every request is validated against current access policies in real-time. Even if an agent attempts to subscribe to unauthorized topics, the broker denies the request at the infrastructure level.
• Audit Trails: Every subscription change and data access is logged, providing complete visibility into what information each agent consumed and when.
• Data Filtering: Event brokers can filter message content, ensuring agents only receive the specific fields they’re authorized to see, even within permitted topics.

These infrastructure-level controls are crucial because they don’t rely on the AI agent’s “cooperation.” Whether due to misconfiguration, prompt injection attacks, or model hallucination, agents might attempt to access inappropriate data. Infrastructure safeguards ensure these attempts fail.

Multi-Source Integration: Breaking Down Data Silos

Modern enterprises have information scattered across cloud applications, on-premises systems, IoT networks, and legacy databases. Agentic AI systems need access to all of it.

Event-driven architectures excel at integrating diverse data sources through a unified event mesh. The bus routing agent might need to consume events from:

• GPS telematics systems
• Passenger counting sensors
• Weather APIs
• Traffic management systems
• Mobile app feedback

Each source publishes events on hierarchically structured topics. The agent doesn’t need to know where the data physically resides—it simply subscribes to the topics that contain the context it needs in the moment, then unsubscribes when no longer needed.

Hierarchical Topics: The Key to Scalable Agent Intelligence

A hierarchical topic structure, such as those used by the Solace event broker , is what makes this architecture scalable. Consider these benefits:

• Granular Context Control: Agents can subscribe at any level of the hierarchy. An agent handling city-wide optimization subscribes to every critical delay, while our Route 47 specialist subscribes to more in-depth information only about Route 47 while solving an issue.
• Natural Security Boundaries: The hierarchy maps naturally to organizational and security boundaries. Different agent types get access to different hierarchy branches.
• Wildcard Flexibility: Using wildcards, agents can dynamically expand or narrow their information scope without hardcoded topic names.
• Efficient Routing: Event brokers can efficiently route events through the hierarchy, ensuring agents only receive relevant data.

The Path Forward

As agentic AI systems become more sophisticated and autonomous, the architecture supporting them must evolve. The combination of event-driven architectures, hierarchical topic structures, and infrastructure-level security controls provides a robust foundation for the next generation of intelligent agents.

This approach delivers what agents need most: the ability to dynamically access contextually relevant information from across the enterprise, persisting while performing their tasks, while maintaining the security and privacy guardrails that enterprises require.

The future of agentic AI isn’t just about smarter models—it’s about smarter architectures that can safely feed those models the right context at the right time. Event-driven architectures aren’t just compatible with this future; they’re essential to it.