Two Voice Devs

Allen and Mark discuss the architecture of Model Context Protocol (MCP)

servers, using Allen's experience with "Vodo Drive" as a case study.

They dive into critical considerations for building effective agents,

focusing on security, managing API complexity, and enforcing business

logic. The conversation explores how to move beyond simple REST API

wrappers to create high-level, context-aware tools that ensure safety

and efficiency.

[00:00:00] Welcome and Introduction

[00:00:54] Lessons from Vodo Drive for MCP

[00:02:54] The Importance of Security in MCP Servers

[00:03:36] Managing API Complexity and Business Logic

[00:05:58] Authentication and Authorization Challenges

[00:07:37] OAuth Scopes and User-Controlled Access

[00:10:48] Handling Complex APIs like Google Workspace

[00:13:58] Designing High-Level Tools vs. Low-Level wrappers

[00:19:35] Dynamic Tool Lists and Context Awareness

[00:24:14] Agents Acting On Behalf of Users, Not As Users

#MCP #ModelContextProtocol #AI #Agents #VodoDrive #Security #API

#GoogleWorkspace #SoftwareArchitecture #TwoVoiceDevs

In this episode of Two Voice Devs, Mark Tucker and Allen Firstenberg dive into the world of GitHub Copilot. Mark shares his recent experience preparing for the GitHub Copilot (GH-300) certification and walks us through the various features and modes of the tool within Visual Studio Code. They discuss the differences between "Ask," "Edit," and "Agent" modes, how Copilot integrates with your workspace and terminal, and the power of using different AI models like Sonnet and Gemini. Whether you're new to AI coding assistants or looking to get more out of your current setup, this episode provides a practical overview of what GitHub Copilot can do today.

[00:00:00] Introduction and updates

[00:01:26] The GitHub Copilot (GH-300) Certification

[00:02:30] GitHub Copilot in Visual Studio Code

[00:03:44] Clarifying the different "Copilots"

[00:04:29] Inline Chat and using "Explain"

[00:05:46] Selecting different AI models

[00:07:39] The Chat Window: Ask, Edit, and Agent modes

[00:08:06] Using context variables (@workspace, @terminal, #files)

[00:11:36] Demonstrating "Ask" mode

[00:14:33] Demonstrating "Edit" mode

[00:16:24] Demonstrating "Agent" mode

[00:22:36] Custom instructions and specifications

[00:25:42] How Copilot works behind the scenes (Proxy & Safety)

[00:27:00] Conclusion

#GitHubCopilot #VSCode #AIcoding #SoftwareDevelopment #TwoVoiceDevs #GH300 #Certification #DeveloperTools #Programming #TechPodcast

Mark Tucker and Allen Firstenberg dive into Microsoft Copilot Studio, a

low-code tool for creating conversational agents with a focus on

enterprise integrations. Mark demonstrates how to build a file upload

agent that summarizes invoices using a Large Language Model (LLM). They

explore the studio's interface, including topics, triggers, and the

designer canvas, while comparing it to familiar tools like Dialogflow.

The discussion also touches on the concept of autonomous agents, flows

that can be triggered by events like emails, and Microsoft's strong

push for enterprise adoption.

[00:00:00] Welcome and Introduction

[00:00:49] Introducing Microsoft Copilot Studio

[00:02:09] Creating a New Agent

[00:05:08] Understanding Topics and Triggers

[00:06:57] Testing the Agent: Greeting Topic

[00:10:44] Building a File Upload Agent

[00:12:37] Implementing the File Upload Logic

[00:15:29] Summarizing Invoices with LLMs

[00:17:08] Enterprise Tools and Connectors

[00:19:00] Flows and Server-Side Triggers

[00:21:33] Deployment and Channels

[00:23:10] Agents vs. Bots: Autonomous Capabilities

[00:24:44] Comparisons with Dialogflow and Google's Ecosystem

#MicrosoftCopilotStudio #CopilotStudio #LowCode #AI

#ArtificialIntelligence #Chatbots #ConversationalAI #EnterpriseAI

#Dialogflow #CCAI #LLM #GenerativeAI #TwoVoiceDevs #Developer #TechPodcast

Allen and Mark return after a hiatus for their annual Thanksgiving episode. They reflect on a busy year, expressing deep gratitude for the community's concern and support. The conversation explores the vital importance of human connection in a tech-centric world, the impact of mentorship, and finding balance between passion projects and life outside of work.

[00:00:00] Welcome back & addressing the hiatus

[00:01:38] Professional gratitude & new projects

[00:02:22] The kindness of the community

[00:03:58] The importance of human connection in tech

[00:05:40] Reflecting on family, friends, and blessings

[00:07:48] The lasting impact of mentorship

[00:09:12] Balancing technology and life

#Thanksgiving #Gratitude #TechCommunity #Mentorship #WorkLifeBalance #HumanConnection #TwoVoiceDevs #VoiceTech #DeveloperLife

Join Allen Firstenberg and Noble Ackerson in a deep dive into the evolving world of AI agent protocols. In this episode of Two Voice Devs, they unpack the Agent-to-Agent (A2A) protocol, comparing it with the Model Context Protocol (MCP). They explore the fundamental differences, from A2A's conversational, stateful nature to MCP's function-call-like structure. The discussion also touches on the new Agent Payment Protocol (AP2) and its potential to revolutionize how AI agents interact and transact. Is A2A the key to unlocking a future of autonomous, ambient AI? Tune in to find out!

[00:01:00] What is the A2A protocol?

[00:04:00] A2A vs. Model Context Protocol (MCP)

[00:10:00] What does A2A bring that MCP doesn't?

[00:15:00] Ambient and Autonomous Agents

[00:19:00] A2A solves the "Tower of Babel" problem

[00:24:00] The difference between A2A and MCP: stateful vs. stateless

[00:27:00] Agent Payment Protocol (AP2)

[00:33:00] What does A2A promise for autonomous agents?

[00:38:00] Downsides and challenges of A2A

[00:44:00] Google, Gemini, and the future of A2A

#A2A #MCP #AI #ArtificialIntelligence #AgentToAgent #ModelContextProtocol #TwoVoiceDevs #TechPodcast #FutureOfAI #AutonomousAgents #AIAgents #AP2 #AgentPaymentProtocol #GoogleGemini #Anthropic

Allen and Mark are back to discuss AI agent frameworks again. This time, Allen compares Google's Agent Development Kit (ADK) with LangChainJS and LangGraphJS. He walks through building a simple agent in both frameworks, highlighting the differences in their approaches, from configuration by convention in ADK to the explicit configuration in LangGraph. They also explore the web-based testing environments for both, showing how each allows for debugging and inspecting the agent's behavior. The discussion also touches on the upcoming LangChain version 1.0 and its focus on backward compatibility.

[00:00:00] - Introduction

[00:01:09] - Comparing agent frameworks: Google's ADK and LangChainJS

[00:02:20] - A look at the ADK code

[00:06:55] - A look at the LangChainJS code

[00:13:20] - The web interface for testing

[00:19:10] - ADK's web interface

[00:22:30] - LangGraph's web interface

[00:27:20] - LangGraph's state management

[00:32:15] - Final thoughts

#AI #AgenticAI #GoogleADK #LangChain #LangGraph #JavaScript #Python #TwoVoiceDevs

In this episode of Two Voice Devs, Mark and Allen dive into the new experimental Text-to-Speech (TTS) model in Google's Gemini 2.5. They explore its capabilities, from single-speaker to multi-speaker audio generation, and discuss how it's a significant leap from the old days of SSML. They also touch on how this new technology can be integrated with LangChainJS to create more dynamic and natural-sounding voice applications. Is this the return of voice as the primary interface for AI?

[00:00:00] Introduction

[00:00:45] Google's new experimental TTS model for Gemini

[00:01:55] Demo of single-speaker TTS in Google's AI Studio

[00:03:05] Code walkthrough for single-speaker TTS

[00:04:30] Lack of fine-grained control compared to SSML

[00:05:15] Using text cues to shape the TTS output

[00:06:20] Demo of multi-speaker TTS with a script

[00:09:50] Code walkthrough for multi-speaker TTS

[00:11:30] The model is tuned for TTS, not general conversation

[00:12:10] Using a separate LLM to generate a script for the TTS model

[00:13:30] Code walkthrough of the two-function approach with LangChainJS

[00:16:15] LangChainJS integration details

[00:19:00] Is Speech Markdown still relevant?

[00:21:20] Latency issues with the current TTS model

[00:22:00] Caching strategies for TTS

[00:23:30] Voice as the natural UI for AI

[00:25:30] Outro

#Gemini #TTS #VoiceAI #VoiceFirst #AI #Google #LangChainJS #LLM #Developer #Podcast

Join Allen and Mark as they take a first look at the newly released GPT-5 from OpenAI. They dive into the details of what's new, what's changed, and what's missing, frequently comparing it to other models like Google's Gemini. From the new mini and nano models to the pricing wars with competitors, they cover the landscape of the latest LLM offerings. They also discuss the new features for developers, including verbosity settings and constrained outputs with context-free grammars, and what this means for the future of AI development. Is GPT-5 the leap forward everyone was expecting, or a sign that the rapid pace of AI evolution is starting to plateau? Tune in to find out!

[00:00:00] Introduction and the hype around GPT-5

[00:01:00] Overview of GPT-5, mini, and nano models

[00:02:00] The new "thinking" model and smart routing

[00:03:00] Simplifying models for developers

[00:04:00] Reasoning levels vs. Gemini's "thinking budget"

[00:06:00] Pricing wars and new models

[00:07:00] OpenAI's new open source models

[00:08:00] New verbosity setting for developers

[00:09:00] Constrained outputs and context-free grammars

[00:12:00] Using LLMs to translate to well-defined data structures

[00:14:00] Reducing hallucinations and medical applications

[00:16:00] Knowledge cutoff dates for the new models

[00:18:00] Coding with GPT-5 and IDE integration

[00:19:00] More natural conversations with ChatGPT

[00:21:00] Missing audio and image modalities vs. Gemini

[00:22:00] Community reaction to the GPT-5 release

[00:24:00] The future of LLMs: Maturing and plateauing

[00:26:00] The need for better developer tools and agentic computing

#GPT5 #OpenAI #LLM #AI #ArtificialIntelligence #Developer #TechTalk #Podcast #AIDEvelopment #MachineLearning #FutureOfAI #AGI #GoogleGemini #TwoVoiceDevs

Join Mark and Allen in this episode of Two Voice Devs as they explore the fascinating world of AI agents. They break down what agents are, how they work, and what sets them apart from earlier AI technologies. The discussion covers key concepts like "context engineering," and the essential components of an agentic system, including prompts, RAG, memory, tools, and structured outputs.

Using a practical example of a prescription management chatbot for veterans, they demonstrate how agents can handle complex tasks. They compare various frameworks for building agents, specifically focusing on OpenAI's Agent SDK (for TypeScript) and Microsoft's Semantic Kernel (for C#). They also touch on other popular frameworks like LangGraph and Google's Agent Developer Kit.

Tune in for a detailed comparison of how OpenAI's Agent SDK and Microsoft's Semantic Kernel handle state, tools, and the overall agent lifecycle, and learn what the future holds for these intelligent systems.

[00:00:00] - Introduction

[00:01:02] - What is an AI Agent?

[00:03:12] - Context Engineering and its components

[00:06:02] - The role of the Agent Controller

[00:08:01] - Agent Mode vs. Agent AI

[00:09:36] - Use Case: Prescription Management Chatbot

[00:13:42] - Handling Large Lists of Data

[00:16:15] - Tools and State Management

[00:21:05] - Filtering and Searching with Tools

[00:27:08] - Displaying Information and Iterating through lists

[00:30:10] - The power of LLMs in Agentic Systems

[00:35:18] - Sub-agents and the future of agentic systems

[00:38:25] - Comparing different Agent Frameworks

[00:39:00] - Wrap up

#AIAgents #TwoVoiceDevs #ContextEngineering #OpenAIAgentSDK #SemanticKernel #LangGraph #GoogleADK #LLMs #GenAI #AI #Developer #Podcast #TypeScript #CSharp

Join Mark and Allen for a very special 250th episode as they celebrate five years of Two Voice Devs! You won't want to miss the unique, AI-animated opening that takes them to new heights, or the special closing that brings it all home, both created with the help of Veo 3. In between, they take a look back at the evolution of voice and AI technology. From the early days of Alexa and Google Assistant to the rise of LLMs and generative AI, they discuss the shifts in the industry, the enduring importance of context, and what the future might hold for agentic AI, security, and the developer experience.

[00:02:45] - Where did we think the industry would be in 5 years?

[00:05:30] - How LLMs and Generative AI changed the landscape

[00:11:05] - Context Engineering is the new Prompt Engineering

[00:14:30] - The explosion of frameworks, libraries, and models

[00:18:00] - The importance of guardrails and security

[00:22:30] - Where are things going in the near term?

[00:27:30] - The future of devices and developer platforms

[00:30:00] - Right-sizing models and the cost of AI

[00:33:30] - The importance of community and having fun

#TwoVoiceDevs #VoiceAI #ArtificialIntelligence #LLMs #GenerativeAI #AIAgents #VoiceFirst #TechPodcast #ConversationalAI #AICommunity #FutureOfTech #AIEthics #AISecurity #DeveloperExperience #HotAirBalloon #Veo3