Fast and Efficient Data Ingestion

1. Fast and Efficient Data Ingestion

Giving our agents access to the most up-to-date and relevant information needed to make decisions and perform actions unlocks additional use cases for agents.

2. Agents without up-to-date information

Without up-to-date information, agents could make bad decisions or perform unwanted actions based on an outdated understanding of the world.

3. Real-time data for multi-agent coordination

Real-time data plays a major part in coordinating multiple AI agents. Let's take our IT support agent as an example. We can create specialized AI Agents for monitoring system logs, another for creating bug reports, and one for analyzing user feedback and responding with troubleshooting steps.

4. Real-time data for multi-agent coordination

These agents need to pass data and context between each other to successfully complete tasks. Slow data exchange between these agents could result in duplicated work, missed connections, and inconsistent actions. For example, the user feedback agent may continue to send troubleshooting steps to users after issues have been resolved.

5. Real-time communication channel

To ensure a continuous, real-time flow of data for shared understanding, we can implement an event-driven architecture. The main approach is to create a shared, real-time, event communication channel for all agents and data tools. Agents can both publish events about their actions and subscribe to events from other agents relevant to their tasks.

6. Real-time communication channel

For example, if the log monitoring agent detected an anomaly, it would publish an "AnomalyDetected" event, which the other agents can immediately consume to update their knowledge.

7. Utilizing an event bus

This channel, known as an event bus, can also be used with other data tools, serving as a unified source of truth for event updates. These sources can include live streaming data and more static databases and vector stores.

8. Utilizing an event bus

To do this effectively, a high throughput and low latency connection needs to be created between the agents and event bus. The Agents then need to be configured to publish new events to the event bus, and only subscribe and consume events that are relevant to their specific tasks.

9. Utilizing an event bus

The data should also go through some preprocessing to ensure its quality and use efficient transmission methods for compression and real-time communication protocols. By using real-time data ingestion and an event-driven approach, we enable our AI agents to operate with complete, up-to-date context, leading to more coordinated operations.

10. Let's practice!

Let's put this into practice with some exercises!

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