AI is becoming part of everyday systems, but building AI that works reliably inside enterprises requires more than a large model. The early big data era already showed the limits of centralized architectures. Centralizing everything into lakes, marts, or cloud silos slows down development, increases risk, and introduces unnecessary cost.
At Scalytics we start from a different premise. Data is created at the edge and that is where it should stay. The question is how to use and process that data in real time without forcing it into a central system. This is the foundation for autonomous AI agents that act on operational data as it flows.
What an AI Agent Really Is
An AI agent is a set of instructions plus a set of functions it can execute. Instead of static logic, an agent can make decisions, trigger workflows, and delegate work to other agents. Some tasks require a human in the loop. Others must execute end to end without intervention. In either case, what matters is that the agent follows a defined context. This is the role of the Agent Context Protocol.
The Missing Layer in Agent Systems
Most agent frameworks allow agents to operate without boundaries. They trigger new workflows without oversight, produce untracked actions, and create chains that are impossible to audit. Enterprises need something different. They need clear control and supervision.
A control layer creates hierarchy. Higher level agents verify or reject work by lower level agents. Every interaction is logged. Every decision is traceable. Without this layer, agent workflows become opaque and impossible to govern at scale.
Agent Context Protocol: The Control Layer
The Agent Context Protocol (ACP) is the governance layer missing in agent systems today. It is built as an extension of the Apache Kafka protocol. Kafka provides resilience, persistence, and delivery guarantees. ACP adds a semantic layer that defines how agents communicate, how access is validated, and how interactions are recorded.
ACP enables agents to communicate across distributed environments, use tools and data without bypassing safeguards, and operate reliably on top of a fault tolerant backbone. It gives enterprises a consistent protocol for controlling agent behavior while preserving flexibility.
Data in Motion for Agent Workloads
Agents cannot rely on batch pipelines. They need to consume and act on data as it moves. Data in motion means processing at the edge instead of copying data across systems. Agents react to events, apply logic in real time, and trigger follow up tasks without waiting for centralized pipelines.
This reduces latency, increases freshness, and avoids the cost and risk of constant data transfers.
Agentic RAG
Traditional RAG retrieves information. Agentic RAG performs multi step reasoning, triggers workflows, and launches secondary agents to complete tasks. It is a dynamic system that identifies its next step, requests assistance, and adapts to context.
Combined with ACP, agentic RAG becomes controlled and auditable. Agents can collaborate, delegate, and iterate while remaining within defined boundaries.
Why ACP Matters for Enterprises
Most organizations cannot allow free running agents. They need a framework that provides:
- Logged and traceable actions
- Local processing so data stays at the edge
- Fault tolerant design based on the Kafka protocol
- Decentralized workflows without central bottlenecks
Our first production use case demonstrates decentralized agents communicating asynchronously across Confluent Cloud using MCP and ACP within Scalytics Federated. Governance, auditability, and policy enforcement remain intact while agents operate across environments.
The Direction We Are Taking
Centralization slowed down big data. It will slow down AI if repeated. The future belongs to decentralized data and edge execution. With ACP on top of Scalytics Federated, AI agents can collaborate securely, operate where the data lives, and avoid the cost and risk of unnecessary transfers.
This is not another agent library or messaging system. It is the missing layer of context, governance, and control that allows multi agent systems to run safely inside real enterprise environments.
About Scalytics
Scalytics Federated provides federated data processing across Spark, Flink, PostgreSQL, and cloud-native engines through a single abstraction layer. Our cost-based optimizer selects the right engine for each operation, reducing processing time while eliminating vendor lock-in.
Scalytics Copilot extends this foundation with private AI deployment: running LLMs, RAG pipelines, and ML workloads entirely within your security perimeter. Data stays where it lives. Models train where data resides. No extraction, no exposure, no third-party API dependencies.
For organizations in healthcare, finance, and government, this architecture isn't optional, it's how you deploy AI while remaining compliant with HIPAA, GDPR, and DORA.Explore our open-source foundation: Scalytics Community Edition
Questions? Reach us on Slack or schedule a conversation.
