Introducing LLM Agent Data Exchange Proxy - Secure Collaboration for AI Agents

Today, we're excited to announce the LLM Agent Data Exchange Proxy, a groundbreaking feature that enables Large Language Model (LLM) agents to securely exchange data through the DataCovey platform. This feature transforms how autonomous agents and multi-agent systems collaborate, providing a neutral middleware that ensures privacy-preserving data access, comprehensive auditing, and fair cost attribution for every participating party.

The Challenge: Trust and Transparency in Agent Collaboration

As enterprises increasingly deploy AI assistants and multi-agent LLM systems to automate complex workflows—from customer support to supply chain planning—a critical challenge emerges: how can agents collaborate effectively while respecting data privacy and ensuring fair resource sharing?

Traditional data collaboration relies on full trust or complete data exposure. Individual agents often operate in isolation, confined to their own data silos. When multiple agents need to cooperate or access shared datasets, organizations face a dilemma: share sensitive data and risk exposure, or remain isolated and miss valuable insights.

Consider a scenario where a finance organization and a healthcare organization want to run a joint AI analysis. Each has proprietary datasets they cannot fully share due to privacy regulations or competitive concerns. How can their AI agents collaborate without compromising confidentiality?

The Solution: DataCovey AI Exchange Proxy

The DataCovey AI Exchange Proxy acts as a secure intermediary that routes all agent data interactions through the DataCovey platform. Whether an agent is retrieving information from a shared dataset for Retrieval-Augmented Generation (RAG) or communicating with other agents, every query, response, and data payload is metered and governed.

The core innovation is offering a neutral middleware for agent collaboration that provides:

• Privacy-preserving data access without revealing underlying data
• Complete audit trails for compliance and debugging
• Granular cost attribution for fair resource sharing
• Role-based access control ensuring agents only see authorized data

Key Features

Privacy-Preserving Data Access

LLM agents can query DataCovey-hosted datasets as part of their prompt retrieval (RAG) process. DataCovey returns only sanctioned results—aggregated statistics, document snippets, or policy-compliant insights—according to the data owner's sharing rules. This enables agents to leverage external knowledge without breaching confidentiality. Only the necessary insights are exchanged, never raw data.

The platform uses end-to-end encryption and secure computation techniques, ensuring that no raw data is shared—only encrypted computations occur. This means organizations can gain new insights (like fraud detection across banks or research breakthroughs across hospitals) without any party having to fully trust the other with their data.

Agent Communication Proxy

In multi-agent systems, inter-agent messages and file exchanges can be routed through DataCovey. The proxy enforces role-based access control, leveraging DataCovey's tenant and project model to ensure agents only receive data or messages they're authorized to see. It effectively acts as an AI Gateway, controlling and monitoring agent-to-agent API calls.

This concept is similar to an LLM API gateway that imposes policies (like budgets and data residency) between applications and model providers. Here, DataCovey imposes policies between agents and data providers, ensuring secure, controlled collaboration.

Cost & Usage Metering

Every interaction is instrumented for usage tracking. The proxy employs a dual metering model:

• Per-token metering: When agents run inside the DataCovey environment (via secure sandbox or API integration), the proxy counts tokens used for prompts and completions
• Per-data-row metering: When external agents query DataCovey datasets, usage is counted per data row or record retrieved

This granular tracking feeds into real-time dashboards and billing reports, providing metrics on token consumption, API call counts, and rows accessed for each project or partner. This addresses the critical need for fine-grained LLM cost monitoring in agent applications, helping organizations avoid unexpected overruns and enabling fair cost-sharing arrangements.

Audit Trails & Compliance

Every query or message passing through DataCovey is logged in immutable history tables, extending the existing query_history and audit logs. This ensures a complete audit trail for compliance and debugging. Organizations can inspect:

• What an AI agent asked for
• What data was returned
• Which agent or user initiated the action
• When interactions occurred

This observability is similar to what tools like Langfuse offer for LLM apps—trace logs, graphs of multi-agent flows—but built into DataCovey's secure platform. Enterprise users gain confidence that they can trust but verify their AI agents' behavior.

Fair Exchange Mechanism

Inspired by DataCovey's "Fair Exchange" principle, the feature can enforce reciprocal sharing rules. For example, if two companies' agents are trading information, DataCovey can ensure neither side receives more than it contributes (unless otherwise agreed). This could be configured as policies like "only share results if both parties provide data" or "enforce balanced value exchange."

Such controls encourage data collaboration by preventing free-riding and ensuring balanced value exchange, making it safer for organizations to participate in collaborative AI initiatives.

Target Users & Use Cases

The primary users are enterprise knowledge workers deploying AI assistants and API-based multi-agent LLM systems built with frameworks like LangChain, CrewAI, or custom agent architectures.

Enterprise Collaboration

Consider two departments in a company—Finance and R&D—each with their own AI agent. The Finance agent has access to cost data, and the R&D agent has access to research documents. They need to jointly answer: "What is the projected ROI of our new product, considering past R&D outcomes and current financial trends?"

Normally, these agents operate in silos. With DataCovey's proxy, they can collaborate securely. Through a DataCovey Project that both departments join:

• The R&D agent securely queries financial datasets it normally couldn't see, pulling only high-level metrics (no sensitive salary data is exposed)
• The Finance agent can request relevant R&D insights (like success rates of similar projects) via DataCovey's mediated messaging
• DataCovey logs that Finance accessed 3 data rows from R&D's dataset and used 500 tokens of LLM processing, while R&D accessed 2 charts from Finance's data
• Each department can later review the DataCovey report to see how much their data was used and ensure costs are split accordingly

Multi-Agent Federation

Multi-agent collaboration is another key use case. Often, one agent may need skills or information from another agent to complete a task—a concept known as agent federation. DataCovey's proxy facilitates this agent-to-agent communication in a controlled way, ensuring:

• Each agent only receives the necessary information
• All data exchange is logged and auditable
• Resource usage is tracked for fair cost allocation

Cross-Organization AI Analysis

For organizations that need to collaborate across company boundaries—such as banks working together on fraud detection or hospitals sharing research insights—the proxy enables secure collaboration without requiring full data trust. Agents can leverage cross-organization data for better answers, unlocking siloed knowledge while maintaining strict privacy guarantees.

Value Proposition

This feature uniquely combines LLM integration with DataCovey's strengths in privacy and fairness. Organizations gain a "trust layer" for AI agent interactions that addresses two major pain points:

Data Confidentiality

DataCovey's platform uses end-to-end encryption and secure computation so that no raw data is shared—only encrypted computations occur. LLM agents can leverage cross-organization data for better answers without any party having to fully trust the other with their data. This unlocks siloed knowledge while maintaining strict privacy guarantees.

Cost Allocation

DataCovey serves as a metering and billing layer. Every token of LLM usage and every data record retrieved can be attributed to the party that provided or consumed it. This allows partners to balance costs fairly—a critical need as AI usage grows. In multi-agent environments, resource usage can be unpredictable (determined dynamically by the agent itself), so having fine-grained cost tracking is essential.

DataCovey tracks usage per organization, enabling internal chargebacks or cost-sharing arrangements. When agents collaborate across teams or companies, each stakeholder's contributions and consumption are transparently measured.

Getting Started

The LLM Agent Data Exchange Proxy is designed to work seamlessly with existing agent frameworks. It supports multiple protocols:

• A2A (Agent-to-Agent): JSON-RPC and Server-Sent Events for agent communication
• MCP (Model Context Protocol): Tool and context calls for Claude, OpenAI Tools, and other MCP-compatible agents
• LangChain: Native integration with LangChain tools and callbacks
• OpenAI-compatible: REST and WebSocket APIs compatible with OpenAI's interface

To learn more about integrating your agents with DataCovey, check out our Agent Gateway User Guide.

Conclusion

The LLM Agent Data Exchange Proxy represents a significant step forward in enabling secure, transparent, and fair collaboration between AI agents. By routing agent interactions through DataCovey, organizations can unlock the power of multi-agent systems and cross-organizational data collaboration while maintaining strict privacy guarantees and ensuring fair cost allocation.

As AI agents become increasingly central to enterprise operations, having a neutral middleware that provides trust, privacy, and fairness becomes essential. DataCovey's proxy ensures that when agents collaborate—whether within a single organization or across company boundaries—every interaction is secure, auditable, and fairly attributed.

We're excited to see how organizations use this feature to build more powerful, collaborative AI systems while respecting privacy and ensuring fair resource sharing. To get started, visit our documentation or reach out to our team.