How I Built The PERFECT AI Agent In 1 Week (And Why I CANT Release It)

Building a Custom AI Agent

Introduction to the Custom AI Agent

• The speaker spent a week developing a custom AI agent capable of reading and sending iMessages, searching Slack and Gmail, and pulling data from App Store Connect.

• The video aims to share insights about the agent SDK, its differences, and how to structure projects using it for better understanding and application.

• The speaker notes that while AI agents have been around for some time, recent hype is due to Enthropic's release of their agent SDK.

Overview of the Custom Agent: Luna L1

• The custom agent named Luna L1 features a mascot based on the speaker's dog and connects with various tools like Slack, Gmail, Notion, etc.

• Unlike other agents such as ChatGPT, Luna L1 can analyze all messages thoroughly to determine urgency rather than just checking recent communications.

• It provides accurate responses even if it takes longer (1-2 minutes), allowing for complex business queries related to app metrics.

Features of Luna L1

• The agent has its own memory system and can run scheduled workflows; it summarizes action items every morning.

• Available on web and iOS platforms, it sends push notifications for urgent matters.

Understanding the Agent SDK

What Constitutes an Agent?

• An agent consists of three main components: an LLM (like Claude or GPT), a set of tools controlled by the LLM, and a loop that executes tasks until completion.

How Agents Operate

• The operational flow involves giving an AI task → selecting tools → executing them → evaluating results → repeating until satisfied with outcomes.

Simplifying Development with Libraries

• Managing conversation history and tool execution can be complex; libraries like Vercel's AI SDK simplify this process by handling loops and tool calls automatically.

Comparing Enthropic’s Agent SDK with Existing Solutions

Key Differences in Functionality

• While both Enthropic’s agent SDK and Vercel’s AI SDK allow for creating agents, Enthropic offers more powerful architecture akin to Claude Code.

Practical Implications

• Although they may appear similar theoretically, practical usage reveals significant differences in performance between the two frameworks.

Agent SDK Features and Benefits

Simplified Conversation Management

• The Agent SDK simplifies conversation history management by providing a session ID, automatically handling context for long-running conversations.

• Automatic compaction is supported, summarizing earlier parts of the conversation to save on token usage without requiring user configuration.

Access to Powerful Tools

• Users gain access to tools like bash commands, file reading/editing capabilities, and web search/scraping tools, which are noted for their high quality.

• The combination of these tools allows users to leverage the same functionalities as Claude Code while tailoring them to specific use cases.

Cost Efficiency with Subscriptions

• Anthropic's subscription model allows users to utilize the agent SDK without incurring additional costs per token consumed; it deducts from existing Claude Code subscriptions.

• A $200 monthly subscription provides approximately $2,000 worth of API tokens, making it financially advantageous for experimentation and development.

Session Management Considerations

• While the SDK manages sessions and context, developers must store conversation data themselves if they wish to display it on a page.

• Sessions have a limited lifespan (around 30 days), so it's recommended that developers implement their own database solutions for persistence.

Database Integration with Convex

• The speaker praises Convex as an effective database solution due to its real-time capabilities and seamless integration with cloud code projects.

• Built-in features such as cron jobs simplify automation tasks without needing separate backend setups, enhancing overall project efficiency.

Agent SDK Tool and Memory Architecture

Overview of Tool Selection and Functionality

• The process begins with choosing a tool, specifically a Zod schema for input formatting and a handler function that executes when the agent is called.

• The SDK provides built-in tools like bash for command execution, file manipulation tools, search functions, and optimized web search capabilities. These are added to an allowed tools array for automatic execution handling.

Skills Integration in Agent SDK

• Skills offer specific capabilities to agents without overloading context; they are organized as folders in the file system following a defined structure.

• Each skill has its own folder containing a metadata YAML file and markdown instructions. Unlike tools, skills are autodiscovered by the SDK upon startup.

Importance of Skill Descriptions

• When users request actions (e.g., checking Slack), the agent identifies relevant skills based on descriptions loaded into the context.

• Effective skill descriptions are crucial; vague descriptions may lead to misinterpretation by the agent regarding tool usage.

Progressive Disclosure of Skills

• The progressive disclosure system loads only necessary skills on demand, minimizing impact on context window despite having multiple installed skills.

• To enable skills properly, they must be included in the allowed tools array with correct configuration settings for source paths.

Memory Architecture Design

• A basic memory architecture categorizes memories into session memory (current conversation), persistent memory (important facts), and archival memory (reference materials).

• The proactive use of memory allows agents to automatically save critical information without explicit user prompts.

Custom Tools for Memory Interaction

• Custom tools facilitate interaction with the memory layer, enabling agents to recognize important information autonomously.

• This approach contrasts with traditional methods where users manually parse conversation history for significant details.

Cost Considerations in Agent Development

• Despite understanding the agent SDK's functionality, releasing custom agents can be prohibitively expensive due to message costs tracked during usage.

Cost Implications and Deployment Challenges of AI Tools

Financial Considerations of Using AI Models

• The speaker discusses the high costs associated with using advanced models like Opus 4.6, noting that significant tool calls can lead to expenses ranging from $2 to $3 per message.

• Current monthly costs for usage are estimated between $200 to $400, but the speaker benefits from a Claude Code subscription that offsets these expenses.

• To sustainably offer a service, the speaker considers charging around $200 per month per user, which seems unfeasible compared to competitors like ChatGPT and Claude offering lower pricing options.

• While consumer use may not justify current costs, there is potential for B2B applications where businesses can rationalize higher expenditures on custom tools.

Limitations Encountered in Development

• Local access limitations hinder functionality; for instance, iMessage integration relies on local SQL database access and fails when deployed on a server.

• Authentication complexity arises due to sensitive API keys required for certain integrations, raising security concerns about sharing these methods with users.

Potential Applications in Business Context

• The speaker highlights successful implementation of a HIPAA compliance agent at their software agency, demonstrating how custom agents can save substantial engineering time and costs.

• Generating reports through this agent saves an estimated $5,000 to $10,000 worth of engineering time while costing only $100 per report.

Future Prospects for Consumer Use

• There is optimism that costs will decrease over time; alternative non-anthropic models could be integrated into the Agent SDK despite current challenges in performance on personal machines.

Deployment Strategies Discussed

• The speaker describes deploying agents via a Virtual Private Server (VPS), allowing continuous operation even when local machines are off.

• A VPS setup enables access to cloud code while maintaining some functionalities unavailable outside Mac environments; toggling between local and VPS connections is preferred based on availability.

• Concerns about secure deployment practices remain prevalent as the speaker continues researching best practices for safely releasing their tools.

This structured summary captures key insights from the transcript regarding cost implications, limitations faced during development, potential business applications of AI tools, future consumer prospects, and strategies for effective deployment.

Features of Productivity Apps

Overview of Features

• The speaker discusses the ability to control a computer directly through various features of productivity apps.

• Emphasizes that there are numerous features not covered in the current discussion, indicating ongoing development and updates.

• Invites viewers to express interest in a more advanced version or part two of the content for deeper exploration.

Engagement with Audience

• Encourages audience interaction by asking viewers to comment if they want more detailed content on specific features.

• Promotes social media platforms (Instagram and TikTok) where the speaker shares insights about building productivity apps regularly.

Call to Action

• Reminds viewers to subscribe if they enjoy this type of content, reinforcing community engagement and support.

• Thanks the audience for their attention, fostering a positive connection with viewers.