Why Agentic RAG Is the Next Big Thing in AI

AI agents, often just “packaged LLMs,” lack robustness and don’t bridge the gap between real data and AI developments. Specialized small agents, capable of repetitive tasks and termination upon completion, are better suited.
Agentic RAG, securely chained and access-restricted to deep infrastructure data, controls communication and execution, with every action audited. It extends traditional RAG systems by integrating external tools and enabling looping function calls, utilizing external resources for data filling, checking, and complex reasoning.

So far, so good.

But there is also an important layer missing – control and command. That’s why we develop a new and open protocol stack by expanding the Model Context Protocol (MCP) into the Agent Context Protocol (ACP). This additional layer enables agent chaining, reducing the possibility of data leakages, and gives developers and data protection officers the control they need to enable autonomous agents to collaborate seamlessly across environments.

Here’s how we’re making it a reality.

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The Basic: Model Context Protocol

MCP, initially designed by Anthropic, is the backbone for managing interactions between LLM-based applications (like Claude Desktop) and diverse data ecosystems. It seamlessly integrates tools with:

• Public data services
• Private and public APIs
• Document archives
• Enterprise-grade data systems

MCP allows LLM-based applications to work together by embedding them into a combined work context, a loose network of  MCP Clients and MCP Servers. It’s the bridge between intelligent tools and the data they need to operate effectively. It’s a great step forward, but not really suited to mission-critical enterprise operations.

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The Leap Forward

Now, when we have a lot of agents running, all more or less independent, how do we make sure they access only data they need, doing what they are intended to do? We think about a kind of an agent firewall, as we know from the SaaS world. There we use application firewalls, secluded areas for applications to access only needed APIs’ in a secure way. Now, why not using the same principle for AI agents?
We call it Data Firewall, a gateway that makes sure that developers and architects have secure data access and governance for AI workloads. We expand agent capabilities, enabling decentralized communication, broad access, and reliable operation.‍

To achieve this we add an open compliance-first protocol between agents and data sources, which consists of three layers.‍

• Governance:
  Tracks usage contexts to enforce secure access to data products.
• Access Control:
  Extends traditional ACLs and RBAC mechanisms by validating the purpose of access. Even with valid credentials, access is denied without a clear, documented reason.
• Auditability:
  Logs every interaction—who accessed what data, when, and for what purpose.

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The ACP protocol layer defines and automates in an open and easy way how enterprises maintain total control over their data environments while enabling productive and collaborative AI workloads.

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‍‍What Is In For You?

While MCP focuses on connecting tools to data, ACP expands these capabilities to enable collaborative and autonomous agents. With Scalytics Connect, agents interact seamlessly with data products and tools, guided by their specific usage context. This represents a breakthrough for enterprise-grade agent systems, setting a new standard for secure and scalable AI collaboration.‍

With ACP, agents can:

• Communicate Decentralized:
  Collaborate securely across different hosts.
• Access Broadly:
  Use tools, data, and memory without infrastructural imposed environment restrictions.
• Operate Reliably:
  Use robust protocols like Apache Kafka for fault-tolerant, asynchronous data exchange.

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Enterprise-Ready Architecture

Scalytics delivers a resilient foundation for agent operations. Agents function within containerized environments, processing data locally while exposing only the necessary results for shared tasks, such as model training or advanced data retrieval.

By leveraging Apache Kafka's proven protocol for resilience, persistence, and delivery guarantees, we enable agents to operate securely in controlled, containerized environments. They process local data safely and expose results only for shared workloads like model training or complex information retrieval—a groundbreaking capability for enterprise-grade agent systems.

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‍Business Impact

Scalytics Connect redefines enterprise AI strategies, enabling organizations to build secure, collaborative, and scalable systems. Here’s how it drives impact:

• Unified Agent Ecosystems
  Scalytics Connect securely links agents and tools across regions and departments, fostering seamless collaboration.
• Effortless Integration
  By leveraging enterprise-grade infrastructure like Apache Kafka, Scalytics Connect integrates into existing systems without requiring costly overhauls.
• Governance and Compliance First
  With a compliance-first gateway, Scalytics Connect enables secure, governed operations, safeguarding enterprise data while maximizing utility.
• Data in Motion
  Agents process and share real-time data securely, adhering to the “data in motion” philosophy critical for modern AI systems.
• AI Readiness
  Scalytics Connect prepares enterprises to scale secure, collaborative AI systems with confidence and precision.

Why It Matters

• AI Infrastructure is Foundational
  MCP and ACP offer a robust, scalable framework designed to make AI enterprise-ready. These protocols provide the foundation for secure and modern AI systems.
• Governance
  Scalytics Connect prioritizes compliance, ensuring data access and usage are transparent, secure, and fully governed.
• Collaboration
  Agents can securely share tools, memory, and data across hosts, enabling innovative workflows and driving better business outcomes.

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Moving Forward - Our Vision

Agentic Retrieval-Augmented Generation (RAG) represents a significant advancement in enterprise AI by integrating autonomous agents capable of dynamic decision-making and real-time data analysis. Unlike traditional RAG systems that passively retrieve and present information, Agentic RAG systems actively engage in multi-step reasoning and tool utilization, enabling more complex and meaningful interactions.

Our first production use case is underway, demonstrating how decentralized agents can communicate asynchronously via Confluent Cloud using the Scalytics Connect MCP implementation. This use case highlights how Scalytics Connect transforms agent collaboration while maintaining compliance and governance within existing data streaming environments.

With Scalytics Connect, enterprises can confidently explore the full potential of their AI strategies and unlock secure, efficient collaboration across their ecosystems.

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