Human reasoning is often mischaracterized as purely logical; it's iterative, intuitive, and driven by communication needs.
AI can be a valuable partner in augmenting human reasoning.
Viewing AI as a system of components, rather than a monolithic model, may better capture the iterative nature of reasoning.
One major criticism of AI today, including the state-of-the-art LLMs, is that they fall short in reasoning capability compared to humans. This criticism often stems from a fundamental misunderstanding of how human reasoning actually works. We tend to hold up an idealized image of human thought – rational, logical, step-by-step – and judge AI against this standard. But is this image accurate?
We want to help you focus on building workflows and enhancing agents, so we're building reference agents to get you going quicker.
Say hello to three new AG2 communication agents - DiscordAgent, SlackAgent, and TelegramAgent, here so that you can use an agentic application to send and retrieve messages from messaging platforms.
In our Adding Browsing Capabilities to AG2 guide, we explored how to build agents with basic web surfing capabilities. Now, let's take it to the next level with WebSurferAgent—a powerful agent that comes with built-in web browsing tools right out of the box!
With WebSurferAgent, your agents can seamlessly browse the web, retrieve real-time information, and interact with web pages—all with minimal setup.
Previously, in our Cross-Framework LLM Tool Integration guide, we combined tools from frameworks like LangChain, CrewAI, and PydanticAI to enhance AG2.
Now, we’re taking AG2 even further by integrating Browser Use and Crawl4AI, enabling agents to navigate websites, extract dynamic content, and interact with web pages. This unlocks new possibilities for automated data collection, web automation, and more.
We previously supported a Realtime Agent powered by OpenAI. In December 2024, Google rolled out Gemini 2.0, which includes the multi-modal live APIs. These APIs enable advanced capabilities such as real-time processing of audio inputs in live conversational settings. To ensure developers can fully leverage the capabilities of the latest LLMs, we now also support a RealtimeAgent powered by Gemini.
In this post, we’ll build upon the concepts introduced in our previous blog on Tools with Dependency Injection. We’ll take a deeper look at how ChatContext can be used to manage the flow of conversations in a more structured and secure way.
By using ChatContext, we can track and control the sequence of function calls during a conversation. This is particularly useful in situations where one task must be completed before another — for example, ensuring that a user logs in before they can check their account balance. This approach helps to prevent errors and enhances the security of the system.
Benefits of Using ChatContext: - Flow Control: Ensures tasks are performed in the correct order, reducing the chance of mistakes. - Enhanced Security: Prevents unauthorized actions, such as accessing sensitive data before authentication. - Simplified Debugging: Logs the conversation history, making it easier to trace and resolve issues.
Note
This blog builds on the concepts shared in the notebook.
TL;DR: - Build a real-time voice application using WebRTC and connect it with the RealtimeAgent. Demo implementation. - Optimized for Real-Time Interactions: Experience seamless voice communication with minimal latency and enhanced reliability.
