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Foundations of Conversational Agency

Conversational Agency

Tools

functions

Guidelines for Tool Definitions

The Tool Usage Workflow

Tool Usage

# Step 1: send the messages to the model along with the tool definitions

# Step 2: append the model's response to the conversation

# (it may be a function call or a normal message)

# Step 3: check if the model wanted to use a tool

# Step 4: extract tool invocation and make evaluation

# note: in python the ** operator unpacks a

# dictionary into keyword arguments

# Step 5: extend conversation with function response

# so that the model can see it in future turns

🎮 Pixel Quest: Tool Usage Protocol

Reasoning and Context

Reasoning

Context for Task-Based Interactions

🎮 Crossword: Agent Terminology

Building the Agent

Building a Conversational Agent

# initialize messages and create preamble describing the agent's

# functionality

# request for user input and append to messages

# if last message is an assistant response then print it

# if it's not a tool call then the assist is waiting

# for the next message—break and wait for input

Optimization and Review

User Experience

Conclusion

Knowledge Check

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Quiz 15

Course Feedback

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Build production-grade AI assistants that don’t just chat—they think, use tools, and get real work done. Building Advanced Conversational Agents with Tools and Reasoning is a hands-on course for developers and product teams who want to design, implement, and optimize reasoning-driven agents powered by Large Language Models (LLMs). You’ll learn the core principles of conversational agency, how to define robust tools and functions, and how to orchestrate end-to-end tool-using loops that interact with APIs, data, and business logic. We’ll demystify frameworks like Chain-of-Thought and ReAct so you can capture reliable reasoning traces without compromising user experience or safety. Through practical blueprints, you’ll integrate context management, error handling, and memory into a cohesive agent architecture, then iterate for latency, accuracy, and clarity. By the end, you’ll have the skills to ship agents that call functions confidently, handle ambiguity gracefully, and deliver measurable value in real applications—from customer support and search to analytics and automation.

9. Building Advanced Conversational Agents with Tools and Reasoning

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            <p>The evolution of artificial intelligence has transitioned from static, text-based interfaces to dynamic conversational agents capable of autonomous reasoning and external interaction. This course provides a comprehensive exploration into the foundations of conversational agency, distinguishing between traditional chatbots and sophisticated agents that utilize reasoning to solve complex tasks. Participants will examine the critical role of tools and executable functions in extending the capabilities of Large Language Models (LLMs), learning to define and integrate external APIs and data sources through rigorous architectural guidelines. By establishing a firm understanding of tool definitions and schema compatibility, developers and researchers can minimize hallucination risks and ensure robust model performance.</p>

<p>Beyond theoretical foundations, the curriculum delves into the technical lifecycle of tool-using conversation loops, encompassing the systematic processing of messages, tool invocation, and the integration of function responses into persistent conversation histories. Advanced methodologies such as Chain-of-Thought (CoT) prompting and the ReAct framework are analyzed to demonstrate how structured reasoning traces enhance agent reliability. The course further guides technology professionals through the practical assembly of a unified system, covering the design of high-level architectures, the implementation of execution loops, and the optimization of user experience. Through a focus on context management and latency reduction, this program equips data scientists and business analysts with the expertise required to deploy high-performance conversational agents in real-world environments.</p>
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