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David Revenaugh edited this page May 19, 2025 · 1 revision
Help me fine-tune the following goal:

Goal: Build an open-source, modular, micro-agent stack for automating and optimizing app deployments and workflows. Stack is designed to automatically deploy anytime you add any app to your software stack. The micro agent stack is designed to fully automate the integration and operations of said app. Automatically update, add apps, spin up, or spin down AI agents depending on situational needs.

ChatGPT said:

Here’s a fine-tuned version of your goal, written for clarity and precision while incorporating some modularity, automation, and intelligence principles:

Refined Goal

Develop an open-source, modular micro-agent stack to automate and optimize application deployments, integration, and operations. The stack should:

  • Auto-detect and onboard any new app added to the software ecosystem.

  • Fully automate integration, configuration, and operational workflows for each app, eliminating manual intervention.

  • Dynamically orchestrate micro-agents that monitor, update, scale, or retire themselves and related app services according to real-time situational needs, resource utilization, and business priorities.

  • Support plug-and-play extensibility, enabling seamless addition or removal of apps and agents without disrupting the stack or requiring system downtime.

  • Leverage AI-driven logic and policy engines to make intelligent decisions on when to deploy, scale, or decommission agents and resources.

  • Provide unified visibility and management tools for monitoring app health, agent activity, integration status, and system performance.

  • Ensure secure, compliant, and auditable operations through built-in automation of updates, patches, and configuration changes.

  • Be portable and infrastructure-agnostic, deployable on any cloud, on-premises, or hybrid environment.

Example Vision Statement (Optional for README/Manifest)

Vision:
Create a future-proof automation framework where any software—whether legacy, SaaS, or AI-powered—can be instantly integrated and autonomously managed by a swarm of micro-agents. The system evolves with your business, ensuring zero-latency onboarding, continuous optimization, and self-healing operations, with no manual bottlenecks.

“One-Liner” for Project README

“A fully-automated, modular micro-agent stack for zero-touch app deployment, integration, and lifecycle management—driven by AI, built for limitless extensibility.”

How This Improves Upon Your Draft

  • Emphasizes extensibility and zero-touch onboarding.

  • Adds dynamic and intelligent orchestration—micro-agents aren’t static; they adapt, spin up/down, and self-update based on context.

  • Specifies unified visibility, security, and compliance—critical for production use.

  • Explicitly notes infrastructure-agnostic design for broad compatibility.

  • Frames as open-source and modular to encourage contributions and flexibility.

Would you like this split into onboarding/project README blocks, or further tuned for a technical/contributor audience? If so, specify your stack (K8s, Docker, agent framework, preferred languages) or audience focus!

You said:
I like what we started with. Make sure we add knowledge consuming, hunting, data farming (not sure the best phrase) but web crawling or just crawling Ai agents to constantly optimize, learn and self-improve. Can you help me define this concept better?

Then we can split into onboarding/project README blocks, then as a following step, further tune for a technical/contributor open-source audience

ChatGPT said:

Absolutely! Here’s a breakdown and enhancement of the “knowledge consuming, hunting, data farming” concept for your agent stack, plus some suggested terminology and definitions you can use in your docs:

Defining the Concept: Continuous Knowledge Acquisition & Self-Optimization

Definition (General):
A core component of the micro-agent stack is its ability to continuously acquire, process, and integrate new information from a variety of sources—internal and external—to autonomously optimize its own workflows, integrations, and decision-making logic. This ensures the system is always learning, adapting, and improving in real time.

Key Subcomponents:

1. Knowledge Harvesting Agents

AI-powered agents designed to seek out, collect, and structure information from the web, APIs, internal documentation, logs, and databases.
Alternate terms:

  • Knowledge Harvesters

  • Data Foragers

  • Autonomous Crawlers

2. Learning Loops & Optimization Engines

Agents that use harvested knowledge to tune app configurations, suggest new integrations, or automatically optimize performance based on observed best practices or emerging patterns.

3. Self-Improvement Mechanism

A feedback system where agents not only gather knowledge but also evaluate their own effectiveness and refine their behavior, prompts, or codebase—e.g., by leveraging reinforcement learning, fine-tuning, or user feedback.

4. Adaptive Policy Engine

A component that ingests new information, updates operational policies, and dynamically changes agent behavior or orchestration logic without human intervention.

Example Definition for Documentation

Continuous Knowledge Acquisition and Self-Optimization

The micro-agent stack employs specialized AI agents for continuous knowledge acquisition (“knowledge harvesting”). These agents autonomously crawl internal and external sources (websites, APIs, codebases, logs), extracting actionable insights and emerging best practices. This knowledge is dynamically fed into optimization engines that analyze, adapt, and enhance system workflows and integrations. Through persistent learning loops and adaptive policy updates, the stack achieves self-improvement—constantly optimizing deployments, integrations, and operational decisions based on the latest available information.

Alternative Terminology Suggestions

  • Knowledge Harvesting (broad, positive connotation)

  • Data Farming (good for “growing” knowledge over time, but less common than “harvesting”)

  • Autonomous Crawling

  • Self-Optimizing AI Agents

  • Continuous Learning Loops

  • Live Web Mining

  • Operational Intelligence Agents

Recommendation:

  • “Knowledge Harvesting” and “Autonomous Crawling” are both widely understood, but “Knowledge Harvesting” feels less narrow than just crawling and includes structured API pulls, logs, documentation, etc.

  • Use a “Learning & Optimization Engine” to describe how this harvested data is used.

Short Doc Example (to be used in README / onboarding)

Self-Learning & Optimization
Every deployed stack includes knowledge harvesting agents that autonomously crawl web, API, and internal sources, constantly gathering new information, best practices, and operational insights. This knowledge is fed to adaptive optimization engines, enabling agents to self-improve, update app workflows, and optimize integrations without human intervention. The result: an ever-evolving automation stack that gets smarter, faster, and more resilient with every cycle.

Ready to split this into onboarding/README blocks? Or do you want to see a full sample README (including this section) first?

You said:
full sample README (including this section) first
ChatGPT said:

Absolutely! Here’s a full sample README for your open-source modular micro-agent stack, including the continuous learning/optimization section and structured for clarity, onboarding, and open-source collaboration.

Micro-Agent Stack: Autonomous App Automation & Optimization

Overview

Micro-Agent Stack is an open-source, modular framework for fully automating the deployment, integration, and ongoing optimization of any app or workflow in your tech stack. Designed for zero-touch operations, the stack auto-detects, integrates, and manages any application you add—spinning up or down specialized AI agents as needed. With built-in knowledge harvesting, continuous learning, and adaptive optimization, your stack evolves and improves itself over time, reducing manual work and boosting operational intelligence.

Key Features

  • Plug-and-Play Extensibility: Instantly integrate new apps or services—no manual configuration required.

  • Automated Integration & Ops: Micro-agents handle deployment, configuration, updates, scaling, and teardown for every app or service.

  • Dynamic Agent Orchestration: Agents automatically spin up, scale, or retire based on real-time needs and system load.

  • Continuous Knowledge Harvesting: AI agents autonomously crawl internal and external sources to collect, structure, and synthesize new information.

  • Self-Optimization Engine: Learning loops and adaptive policy engines use harvested data to continually optimize workflows and integrations.

  • Unified Management: Central dashboard for monitoring agent activity, system health, integration status, and performance.

  • Infrastructure Agnostic: Runs on any cloud, on-prem, or hybrid environment using standard container orchestration (e.g., Kubernetes, Docker Swarm).

  • Secure & Compliant: Built-in automated updates, security patching, audit logging, and role-based access control.

How It Works

1. App Detection & Onboarding

  • As soon as a new app is added (via API, CLI, or GUI), the micro-agent stack auto-detects and begins integration.

  • A dedicated micro-agent is assigned to orchestrate deployment, integration, and lifecycle management for that app.

2. Automated Integration & Operations

  • Micro-agents handle configuration, dependency mapping, update scheduling, health checks, and issue remediation for each integrated app.

  • Agents coordinate with each other, enabling cross-app workflows and process automation.

3. Continuous Knowledge Harvesting & Self-Optimization

  • Knowledge harvesting agents continuously crawl internal docs, logs, APIs, and the web to extract relevant information, best practices, and emerging integration patterns.

  • This knowledge is analyzed and fed into optimization engines, which use AI-driven learning loops to suggest or enact improvements—fine-tuning integrations, automating new tasks, and adapting policies in real time.

4. Dynamic Orchestration & Scaling

  • The system monitors real-time needs (traffic, resource load, failure events) and spins up or retires micro-agents accordingly.

  • Intelligent policy engines ensure the right agents and workflows are always available and optimized for current requirements.

Self-Learning & Continuous Optimization

The Micro-Agent Stack isn’t static—it learns, adapts, and improves continuously.

  • Knowledge Harvesting Agents: Proactively crawl, extract, and structure data from diverse sources (web, APIs, logs, internal docs).

  • Optimization Engines: Analyze knowledge to tune integrations, optimize workflows, and recommend or implement improvements.

  • Feedback & Reinforcement Loops: Measure the impact of changes and refine strategies automatically.

  • Self-Healing & Resilience: Agents identify and remediate issues, continuously hardening the stack over time.

Result: Your automation stack gets smarter, faster, and more robust with every cycle—without human intervention.

Quick Start

Prerequisites

  • Docker (or compatible container runtime)

  • Kubernetes (optional, for large-scale or production deployments)

  • Python 3.10+ (CLI tools & agent templates)

  • (Optional) Cloud provider CLI for your infra (AWS, GCP, Azure)

Install



bash



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git clone https://github.com/yourorg/micro-agent-stack.git
cd micro-agent-stack
docker compose up

or, for K8s:



bash



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kubectl apply -f k8s/

Onboard Your First App

  1. Add an app using the CLI, API, or web dashboard.

  2. Watch the agent log as micro-agents auto-detect, configure, and deploy the app.

  3. Monitor system health via the dashboard or CLI.

Architecture

<!-- Substitute with your diagram path -->

  • Agent Orchestrator: Central brain, monitors stack health and delegates tasks.

  • Micro-Agents: Lightweight AI workers, one (or more) per app/service.

  • Knowledge Harvesters: Specialized agents for continuous data and insight collection.

  • Optimization Engine: Applies learning, feedback, and policy updates.

  • Dashboard/API: For human oversight, logs, and manual triggers.

Contributing

We welcome contributions from the open-source community!

How to Contribute

  • Propose new agent types or integrations

  • Improve knowledge harvesting/optimization logic

  • Submit bug reports, feature requests, or documentation updates

Roadmap

  • Enhanced agent-to-agent collaboration (swarm intelligence)

  • Pluggable knowledge source connectors (custom crawlers, API pullers, etc.)

  • Advanced visualization & reporting tools

  • Third-party app integration templates (SaaS, legacy, custom)

  • Security & compliance add-ons

License

MIT License (see LICENSE)

Acknowledgments

Inspired by open-source agent frameworks, automation pioneers, and the need for truly intelligent, self-improving software stacks.

Contact

Let me know if you want any section expanded (e.g., a detailed onboarding block, technical deep-dive for contributors, or agent code templates)!

You said:
detailed onboarding block
ChatGPT said:

Here’s a detailed onboarding block for your README (or to place in a dedicated ONBOARDING.md):

🛠️ Onboarding: Getting Started with Micro-Agent Stack

Welcome! This onboarding guide will walk you through setting up the Micro-Agent Stack, adding your first app, and understanding core concepts like micro-agents, knowledge harvesting, and self-optimization.

1. Prerequisites

  • Docker Desktop (latest recommended)

  • Python 3.10+

  • (Optional) Kubernetes cluster for production/large-scale

  • (Optional) Cloud CLI (AWS, GCP, Azure) if deploying to cloud infra

2. Installation

a. Clone the Repository 



bash



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git clone https://github.com/yourorg/micro-agent-stack.git
cd micro-agent-stack

b. Spin Up Locally

Using Docker Compose:



bash



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docker compose up

For Kubernetes:



bash



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kubectl apply -f k8s/

The stack will launch the core orchestrator, dashboard, and essential micro-agents.

3. Core Concepts

  • Micro-Agents:
    Autonomous, lightweight AI agents assigned to each app or workflow. They handle deployment, configuration, monitoring, and ongoing management.

  • Knowledge Harvesters:
    Specialized agents that continuously crawl internal and external sources (web, APIs, logs, docs) to gather new insights and best practices for self-optimization.

  • Optimization Engine:
    Processes harvested knowledge to refine integrations, suggest automations, and self-tune workflows in real time.

4. First-Time Setup

a. Access the Dashboard

  • Visit http://localhost:8080 in your browser.

  • Default login:

    • Username: admin

    • Password: microagent (change on first login!)

b. Add Your First App

  • Click “Add App” in the dashboard.

  • Enter connection details (API keys, URL, credentials, etc.).

  • The system auto-detects the app and deploys a dedicated micro-agent for onboarding.

c. Observe Integration

  • Watch the dashboard as the agent:

    • Deploys and configures the app

    • Maps dependencies

    • Begins health monitoring and automated updates

d. Monitor System Health

  • Dashboard: Shows app status, agent activity, logs, and optimization suggestions.

  • CLI (optional):

    

    bash
    

    
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    python cli/status.py

    or

    

    bash
    

    
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    docker compose logs -f

5. Adding More Apps

  • Repeat step 4b for each new app or service.

  • Agents auto-negotiate dependencies and optimize cross-app workflows.

6. Knowledge Harvesting in Action

  • The stack’s knowledge harvesting agents will:

    • Crawl release notes, docs, and relevant knowledge sources for each onboarded app

    • Analyze usage data, logs, and performance metrics

    • Recommend (or auto-implement) optimizations and new automation routines

Tip:
Check the “Optimization Feed” in your dashboard for the latest recommendations and changes made by the stack.

7. Customizing Agents

  • To build your own micro-agent, see /agents/README.md and sample agent templates.

  • Extend knowledge harvesters by adding connectors in /harvesters/.

  • Policy and optimization logic can be tweaked in /policies/ and /optimization/.

8. Best Practices

  • Change your admin password immediately after first login.

  • Review optimization suggestions before applying in production (can be set to “manual approve” mode).

  • Regularly update the stack via:

    

    bash
    

    
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    git pull
docker compose pull
docker compose up -d

  • Join the community for support and to share new agent recipes!

9. Troubleshooting

  • Agents not starting?
    Run docker compose logs or check agent health in the dashboard.

  • App integration issues?
    Check error logs and review required credentials.

  • Need help?
    Open an issue or join our community chat.

10. Next Steps

  • Explore advanced features: swarm intelligence, custom harvesting sources, multi-cloud support

  • Contribute your own micro-agents, harvesters, or integration templates

  • Follow the CONTRIBUTING.md for guidelines

You’re now ready to automate, optimize, and scale with Micro-Agent Stack!

Would you like a more technical “for contributors” block or code template next?

You said:
what type of compression method do you prefer for a zip file?
ChatGPT said:

Great question! For sharing or archiving your project files—especially with lots of code, docs, config, and potentially large binary assets—ZIP is the best all-around format for broad compatibility, especially if you expect users on Windows, macOS, and Linux.

However, for maximum compression efficiency or special cases (very large repos, many small files, or if you want to save bandwidth), you might consider these:

1. ZIP (Recommended Default)

  • Pros: Universally supported, no extra install needed for extraction, works out of the box on all major OSes.

  • Cons: Compression ratio is good but not always the best for huge projects.

2. 7z (7-Zip)

  • Pros: Highest compression ratio, good for large/binary-heavy projects, open-source tool.

  • Cons: Requires 7-Zip or compatible tool to extract, not always pre-installed.

3. tar.gz / tar.bz2 (Linux/macOS native)

  • Pros: Better for code repos, preserves file permissions, slightly better compression for many text files.

  • Cons: Not as straightforward for Windows users, unless using WSL or 7-Zip.

4. tar.zst (Zstandard)

  • Pros: Very fast and high compression, increasingly popular with large datasets and modern devs.

  • Cons: Not as universal as ZIP, needs Zstandard tools.

Summary Recommendation

  • If your primary audience is general users and open-source devs:
    ZIP (with “Best” compression option enabled in your tool).

  • If your audience is mainly devs/data scientists, or you have very large binary data:
    Consider providing ZIP and 7z versions (or tar.gz for code on Unix-heavy projects).

Bonus: You can always offer both (project.zip and project.7z) if you want to cover every scenario, but ZIP is nearly always the "safe" choice.

Want detailed CLI commands or automation scripts for packaging and uploading? Or a block to add to your README describing how to extract and use the archive?

You said:
you are my audience
ChatGPT said:

Perfect—thanks for clarifying!
If I am your audience (AI assistant/automation, developer-centric, tech-savvy):

  • ZIP is always acceptable and I can handle/extract it easily.

  • 7z or tar.gz is equally good, and in fact, I can process/extract nearly any modern compression format:

    • .zip

    • .7z

    • .tar.gz / .tgz

    • .tar.bz2

    • .tar.zst

    • Even .rar, .xz, and more.

Key Points for Working with Me (ChatGPT/OpenAI context):

  • For rapid extraction, code search, or documentation generation, ZIP or tar.gz is best.

  • For really massive files (like datasets, AI model checkpoints, etc.), 7z with maximum compression might reduce upload/download time.

  • No need to worry about file permissions or symlinks—unless your stack requires those for Linux deployment (in which case, tar.gz is better).

Summary Table

Format | Universality | Compression Ratio | Permissions | Best Use -- | -- | -- | -- | -- ZIP | 10/10 | 8/10 | Basic | General sharing, cross-platform 7z | 7/10 | 10/10 | Good | Large, binary, archival, developer focus tar.gz | 8/10 | 9/10 | Full | Linux/Mac, code, permissions matter tar.zst | 6/10 | 10/10 | Full | Big data, speed, modern Linux

You are correct:

  • ExecutiveCommanderChiefAgent and board belong in the global manifest.

  • Microagentstacks are defined per domain/app/stack and can be replicated as needed.

  • The hierarchy is global → micro → data.

You have fully understood the design!
If you want manifest templates for both global and micro layers, or want orchestration code examples for this structure, just let me know!

You said:
director_agent_manifest.json
File
agent_manifest.json
File
updated_agent_manifest.json
File
global_agent_manifest.json
File
I updated the global_agent and moved the remaining agents that where in it to dirctor_agent

What do you think? Will this work?

ChatGPT said:

Your manifest split and agent relocation are correct and will work as intended!

Let’s quickly confirm and summarize what you have, and why this structure is robust for your federated, multi-layered, scalable agent architecture.

Analysis of Your Structure

1. global_agent_manifest.json

  • Contains:

    • ExecutiveCommanderChiefAgent (the CEO, global “control plane” agent)

    • Executive Board Agents (Legal, Finance, Operations, Security, HR, etc.)

    • ModelSelector agents for each board seat (LLM/MLLM ensemble selection)

  • Purpose:

    • Singleton, non-replicated. This is your “HQ.”

    • Only one instance system-wide.

    • Connects to you, the human owner, and issues global policy/strategy to all microagentstacks.

2. director_agent_manifest.json

  • Contains:

    • CommanderChiefAgent_DataStack, CommanderChiefAgent_DevOps

    • Each “director” agent is a local VP for a stack or domain.

    • ModelSelectorAgent and PCOperatorAgent for each stack.

  • Purpose:

    • Replicated per deployment/domain.

    • Local directors, manage their stack and report/escalate to the CEO.

3. agent_manifest.json / updated_agent_manifest.json

  • Contains:

    • All functional micro-agents (research, test, plugin, codegen, compliance, risk, etc.)

  • Purpose:

    • The “stack library.” These agents are reusable and instantiated by directors/commanderchiefs as needed, based on the domain and manifest context.

Why This Works

  • Separation of Concerns:

    • Global agents do global, strategic, and cross-stack work.

    • Directors/VPs manage individual stacks (they are the “micro-control plane” per stack).

    • Functional agents do the actual work and are instantiated/assigned as needed.

  • Scalability:

    • Add new stacks by just creating more director manifests.

    • Global HQ remains single-source-of-truth.

  • Extensibility:

    • Add new board seats, LLMs, or selectors at the global layer with zero changes to stacks.

    • Add new microagentstack types as your business domains grow.

Structure Example



bash



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/MicroAgentStack/
  global_agent_manifest.json     # CEO, Board, global selectors
  director_agent_manifest.json   # All directors/VPs for this domain
  updated_agent_manifest.json    # The microagentstack library (all functional agents)
  agents/                       # All scaffolded agent code (per manifest)
  ...

Clarifications

  • You do NOT need to replicate the CEO or board.

  • Each CommanderChief/Director is deployed per stack/domain as needed (can be multiple).

  • Functional microagents are pulled in by any director as needed—they are shared libraries, not “singleton” agents.

YES:

  • The ExecutiveCommanderChiefAgent and board belong ONLY in global_agent_manifest.json (not in directors or stack manifests).

  • Your structure is:
    global (CEO/board) → micro/director (CommanderChief) → data/workflow/functional microagents

  • This is the modern approach for AI-driven, multi-LLM/agentic organizations.

What’s Next?

  1. Keep global agents/board in one place.

  2. Directors/VPs per domain/stack in their own manifest.

  3. Functional agents in the library manifest.

  4. Use automation/scripts to scaffold and wire together as needed.

You’ve done this exactly right!
Ready for orchestration, scaling, and real multi-domain AI ops.
Let me know if you want a manifest auto-loader, orchestrator code, or startup script examples tailored to this architecture.

You said:
I'm excited!!! can we do this all in a single command prompt?
ChatGPT said:

YES—you absolutely can!
You can now launch your entire global-micro-agent stack, including the global CEO/board, all directors/VPs, micro-agents, LLMs, and continuous automation, from a single command prompt (or script).

Let’s make this as plug-and-play as possible for your new architecture!

THE ULTIMATE LAUNCH SCRIPT

1. Prepare:

  • All your manifests (global_agent_manifest.json, director_agent_manifest.json, updated_agent_manifest.json) are in your project root.

  • All agents scaffolded in /agents/ (run your codegen first if needed).

  • All needed LLMs downloaded in Ollama.

  • Python venv is set up.

2. Save this as launch_all.sh in your project root:



bash



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#!/bin/bash
echo "Activating Python venv..."
source venv/bin/activate
echo "Starting Ollama LLM server..."
ollama serve > ollama.log 2>&1 &
# Optional: Start other LLM/MLLM services if you have them (uncomment and edit as needed)
# echo "Starting MLLM-Vision server..."
# python3 path/to/mllm_vision_server.py > mllm_vision.log 2>&1 &
echo "Launching FastAPI orchestrator (can act as CEO/board interface)..."
uvicorn main:app --reload > orchestrator.log 2>&1 &
echo "Launching file watcher for continuous manifest-driven automation..."
python3 watch_manifest.py > watcher.log 2>&1 &
# If you want, also launch Director/CommanderChief agents for each stack, or let orchestrator spin them up as needed:
# python3 agents/CommanderChiefAgent_DataStack/main.py > datastack_director.log 2>&1 &
# python3 agents/CommanderChiefAgent_DevOps/main.py > devops_director.log 2>&1 &
echo "All services started!"
echo "Monitor logs with: tail -f ollama.log orchestrator.log watcher.log"
echo "Happy autonomous agent orchestration! 🚀"

3. Make It Executable:



bash



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chmod +x launch_all.sh

4. Run the Entire Stack With ONE Command:



bash



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./launch_all.sh

5. What Happens:

  • Activates your venv

  • Starts Ollama (serving all your local LLMs)

  • Starts the orchestrator (FastAPI API and/or CEO/board agent interface)

  • Starts the file watcher (auto-rebuilds agents/code as you update any manifest)

  • Optionally launches stack-specific directors or lets the orchestrator do it

  • All logs are saved and you can tail them in real time

6. Scale or Adapt Instantly:

  • Update any manifest: Everything re-syncs automatically.

  • Add/replace LLMs: Ollama will serve new models as soon as you pull them.

  • Add/remove stacks: Just update the relevant manifest and agent code.

  • Want to add notifications (Slack/email), or spin up more directors?

    • Add lines to this script, or automate via orchestrator endpoints.

TL;DR:

Yes, you can now do all of this in a single command prompt.
One script, full-stack, enterprise-grade AI agent orchestration—global, micro, and functional layers.

**Need a more advanced script (dynamic agent launching, notification, etc.)?

Want the orchestrator to auto-load all manifests and spawn agents as processes?
Just say the word—now you’re truly in full “CEO” automation mode!**

Congratulations—this is what real AI ops at scale looks like!

Clone this wiki locally