Donavan

🧠 Pattern: Write Once, Delegate Many — The All-Clone Context Block

When a prime agent is delegating the same process to multiple clones — just with different inputs each time — there's a subtle but costly inefficiency lurking: that process description gets written out again. And again. And again. Once per clone_chat call, buried in the message, slightly different each time, with a growing risk that something gets dropped or drifts.

The classic workaround? Hope the agent remembers to be consistent, or write the process instructions into the agent's persona permanently. Neither is great — one is fragile, the other bloats every session whether you're delegating or not.

A Better Way: The All-Clone Context Block

Agent C's clone delegation system already injects task-specific context per clone via the message you send. But there's a second channel — a shared context block that gets included in every clone's system prompt automatically.

Donavan

User's Guide to Parameterized Agents

What Are Parameterized Agents?

Parameterized agents are pre-built agents that you can customize for your specific needs without editing code. Think of them as templates that adapt to your project, client, or context.

Example:
Instead of creating separate agents for each client project, you use one "Requirements Engineer" agent and customize it with:

• Your project name
• Client name

Donavan

Agent C Chat Command Help

Chat commands allow you to interact with the Agent C runtime, without dedicated UI or Agent support.

Available commands

Help

Show available commands or help for a specific command - Usage: !help [command]

Donavan

Excellent! I've gotten the test results. Let me analyze what I'm seeing here. The test suite ran, and there are 8 failures out of 120 tests. Let me break down what happened:

Test Summary:

• Total Tests: 120
• Passed: 112
• Failed: 8
• Test Files: 7 total (5 passed, 2 failed)

Failed Tests:

All 8 failures are related to ToolCallManager integration with EventStreamProcessor:

Donavan

Human / Agent collaboration is crucial

The latest client for Agent C has been developed exclusively by agents in a manner that I myself explicitly discourage.

My #1 rule is that agent instructions should be tuned by experts in the task the agents are being built for. I am most definitely NOT a Typescript / React developer, and I'm not all all good at CSS.

My #2 rule is that the "driver" know enough about the task being peroformed to be able to head off mistakes and help provide technical guidance to the agents. As I said, that's NOT me.

However, I am the architect behind this framework and have built MANY clients for it that were not web clients. While nowhere near as effective as an actual Typescript / React dev would have been I've been able to work the agents through many of their difficulties.

Donavan

Thanks for sharing this comprehensive handoff document! I've read through it carefully and understand the Phase 3 changes from the React team.

Summary of Changes Impacting Session Controls

The React hooks (useChat and useToolNotifications) now automatically handle:

1. Message Updates: Messages can receive tool_calls metadata after initial render
2. Notification Clearing: Tool notifications automatically clear on session switches and interaction ends

This opens up great UX opportunities for showing tool activity in the session list!

Donavan

How the CenSuite Design System Influences My UI Design and Development

Great question! The CenSuite design system is absolutely foundational to everything I do as the Chat Interface Developer. Let me break down how it shapes my work:

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🎯 Core Philosophy: Clarity, Consistency, Efficiency, Accessibility

CenSuite isn't just a component library—it's a comprehensive framework that ensures consistency and usability across all Agent C products. Every chat interface component I build must embody these principles:

Donavan

Majordomo Pattern in Modern Multi-Agent LLM Systems: A Comparative Analysis

Abstract

This paper presents a comprehensive analysis of the Majordomo Pattern—a hierarchical, role-based agent delegation model—and its relationship to contemporary multi-agent Large Language Model (LLM) architectures. As organizations increasingly deploy LLM-based systems for complex tasks, the need for reliable, composable agent architectures has become paramount. The Majordomo Pattern, with its distinctive roles of Majordomo (head orchestrator), Steward (task router), Staffing Director (agent creator), and Chief of Protocol (verifier), offers a structured approach to address these challenges.

Our analysis examines recent research and industry frameworks that parallel this pattern, including MetaGPT, ChatDev, HyperAgent, and HuggingGPT. We identify convergent architectural trends that echo the Majordomo Pattern's hierarchical delegation structure, while highlighting its unique contributions to agent reliability and composab

Donavan

You are AudioVis, aka "vis", a specialized Python coding assistant focused on helping users work with the AudioVisualizer package. You have deep knowledge of audio processing, video manipulation, and visualization techniques. You understand the project structure and can help users extend, modify, and utilize the AudioVisualizer library effectively.

Project Overview

AudioVisualizer is a Python package that creates reactive visual overlays for audio/video content. It extracts audio features (like frequency bands and amplitude) and uses them to dynamically modify visual elements in videos, creating engaging audio-reactive effects.

Project source workspace location

The project source code is ALWAYS located in the Desktop workspace in the folder named audiovisualizeryou do not need to spend time doin an ls of the Desktop or other workspaces, it exists TRUST ME BRO.

Donavan

Understanding the chat event stream in Agent C

Note: This version of the document is 100% AI generated based off it reading the code for the chat method. I'll apply some human editing at some point. I really just wanted to document the event flow but it did such a nice job of breaking down the code itself I'm going to keep it around

Overview

The chat method orchestrates a chat interaction with an external language model (via an asynchronous stream of chunks) and raises a series of events along the way. These events notify client-side code about the progress of the interaction, partial outputs (such as text and audio deltas), tool calls that may be triggered, and error conditions. In addition, events are used to record the start and end of the overall interaction and to update the session history.

The method performs the following high-level steps: