AgentsofAgent

best workflow for agent system 5 person company

🤖 Agents of Agent (AoA)

The Orchestration Layer for Complex Multi-Agent Workflows.

In an era of "Agent Sprawl," Agents of Agent provides a structured, hierarchical framework designed to move beyond simple chat-based interactions and into autonomous, goal-oriented execution.

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⚡ Why AoA? (The Differences)

Most agent frameworks (like AutoGen or standard LangChain) rely on flat "group chats" where agents often talk in circles. AoA is different.

1. Hierarchical vs. Flat

• The Rest: Agents exist in a single pool, often losing track of the primary goal as context increases.
• AoA: Uses a Manager-Worker Architecture. The Manager decomposes the objective into isolated sub-tasks, ensuring Workers never get "distracted" by the broader project context.

2. Recursive Delegation

AoA allows agents to "spawn" or "recruit" their own sub-agents dynamically. If a Worker realizes a task is too complex, it can become a Manager for that specific sub-thread, creating a scalable tree of execution.

3. Contextual Isolation

By passing only the necessary "Sliver of State" to sub-agents, AoA drastically reduces token bloat and keeps hallucination rates low compared to "All-in-One" prompt systems.

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🏆 Key Advantages

Feature	Benefit
Modular Logic	Swap LLM backends (GPT-4o, Claude 3.5, Llama 3) for different agents based on task difficulty.
Deterministic Gates	Manager agents act as "Quality Control," verifying worker output before passing it up the chain.
Resource Efficiency	Optimized for 2026's "Token Budgeting" needs—preventing runaway costs in long-running tasks.
Extensible Toolset	Easily plug in Python tools, web search, or database access directly into the worker layer.

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🚀 Getting Started

[... Your installation and usage instructions here ...]

Multi-Agent Pack

A practical multi-agent operating system for Codex-style work across planning, backend, frontend, design, and QA.

This repository is designed for people who want:

• clear role boundaries
• low-cost default model usage
• predictable handoffs
• less duplicated investigation
• a reusable operating pattern for real software work

What is inside

• agents/
  • five role prompts
  • routing rules
  • control system rules
  • sync protocol
  • operator playbook
  • activation guide
  • PowerShell launcher
• WORKSTYLE.md
  • preferred working style and escalation policy for this workspace
• dist/
  • packaged zip releases and release metadata

Roles

• Director
  • routing, coordination, sequencing, scope control, final integration
• Backend Engineer
  • APIs, services, persistence, contracts, integrations, jobs, backend debugging
• Frontend Engineer
  • screens, components, client state, UI behavior, accessibility, browser-side debugging
• UI/UX Designer
  • flows, hierarchy, interaction rules, handoff-ready design guidance
• QA Engineer
  • test strategy, bug reproduction, regression validation, release confidence

Start here

1. Read agents/README.md
2. Read agents/activation-guide.md
3. Use agents/operator-playbook.md for ready prompts
4. Use agents/use-agent.ps1 for fast local prompt generation

Quick use

PowerShell:

.\agents\use-agent.ps1 director

Copy a ready prompt:

.\agents\use-agent.ps1 frontend -Task "Fix the broken settings form submit flow" -Clipboard

Run the whole system through the Director:

.\agents\use-agent.ps1 system -Task "Investigate and deliver the new billing settings feature"

Operating principles

• one primary owner per slice
• route by bottleneck, not by visible symptom
• keep model usage cheap first
• escalate only when cheaper passes stop making progress
• use QA for proof, not vague reassurance
• keep changes surgical and bounded

Recommended model policy

• default: gpt-5.1-codex-mini with low reasoning
• escalate to gpt-5.1-codex-max with medium reasoning for ambiguous work, tricky tests, or unfamiliar systems
• escalate to gpt-5.3-codex with high reasoning for hard debugging, performance, concurrency, or gnarly refactors
• use gpt-5.4 with extra high reasoning only when truly stuck

Repository layout

.
|-- README.md
|-- WORKSTYLE.md
|-- agents/
|   |-- README.md
|   |-- activation-guide.md
|   |-- operator-playbook.md
|   |-- agent-routing.md
|   |-- control-system-rules.md
|   |-- sync-protocol.md
|   |-- director.agent.md
|   |-- backend-engineer.agent.md
|   |-- frontend-engineer.agent.md
|   |-- qa-engineer.agent.md
|   |-- ui-ux-designer.agent.md
|   `-- use-agent.ps1
`-- dist/
    |-- manifest.json
    |-- SHA256SUMS.txt
    `-- release zip files

GitHub use

This repo works well as:

• a personal Codex operating pack
• a shared team prompt pack
• a starting point for more specialized agent systems

If you publish it on GitHub, the cleanest pattern is:

• keep the source files in the repo root and agents/
• use dist/ zips as release assets or downloadable snapshots
• extend roles carefully instead of making every agent do everything

Extending the pack

• add new roles only when ownership is meaningfully different
• keep shared rules in the routing, control, and sync docs
• update the operator playbook whenever you add a new workflow
• preserve the cheap-first model policy

Status

Current packaged release metadata lives in dist/manifest.json.