Mastering Generative AI Agents: Balancing Autonomy and Control (1/3)
Generative AI Agents based on Large Language Models are powerful tools that excel in autonomously managing complex scenarios. They blend two core functions: generative conversational interaction and automation. The latter takes a more technical approach reminiscent of Software Agents, while the former represents an evolution of traditional chatbots. However, generative autonomy comes with risks – AI Agents can lose focus, much like a chatty human, potentially straying off track.
This article series dives into strategies to mitigate these risks and optimize user experiences. From managing generative conversations to incorporating multimodal features like galleries and quick reply buttons, discover how to harness the full potential of AI agents while maintaining control and delivering an engaging, seamless interaction. Let's begin with the first part, demonstrating how to control the flow of a conversation...
Conversational Flow
Understanding the flow of conversations with AI Agents is essential for controlling and optimizing interactions. Typically, AI Agents generate responses autonomously based on the provided instructions (prompt engineering), context, and tools available within the AI Agent Node. Responses are displayed immediately unless specific settings are configured to disable this behavior.
Before diving into the technical possibilities, it’s often more effective to adopt the general AI Agent mental model: think of an AI Agent as a linguistically talented intern equipped with a detailed handbook outlining their tasks, responsibilities, tools, and necessary knowledge. Typically, you wouldn’t provide precise instructions or rules for individual conversational flows unless explicitly needed.
Output Responses Immediately
There are numerous options to control the output within the defined guardrails at the AI Agent Node level. These settings can be adjusted in the Storage & Streaming Options section of the node's settings:
Tip: You can always use a Transformer Function in your endpoint settings to format the content before sending it to the output channel. For example, you can convert markdown to HTML or SSML independently from the conversation, ensuring compatibility with platforms like a webchat or a voice gateway.
Tools and Resolve Tool Actions
It’s important to understand that the AI Agent can produce a result regardless of whether a tool is used. Tools and textual outputs are not mutually exclusive. Within a tool branch, you have the flexibility to explicitly generate an output – for example, by using a Say Node—or delegate the response generation back to the AI Agent by using the Resolve Tool Action. The latter simply starts a new turn, and the AI Agent Node takes over again to manage the next steps.
Processing Responses
You can store the AI Agent's response in either the input or context object for further processing. This allows you to control and change the response using all the tools available in Cognigy.AI. However, as mentioned earlier, this approach is less effective if the response is streamed or output immediately – unless you're performing post-processing, for instance, for purposes such as moderation or analytics.
If you store the AI Agent’s result in the input, you can access it using CognigyScript, for instance, with {{input.aiAgentOutput.result}}, which can be used for further processing before immediate output, using, for example, a Say Node. You can also use this within a tool's branch, but a textual result is not guaranteed in this context.
In the next part, we'll explore Large Language Model-based tool calls and how these functions can be utilized to guide and manage a generative conversation...