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Marty McEnroe edited this page Feb 5, 2026 · 12 revisions

AgentOS

Multi-Agent Orchestration Platform for Enterprise AI Development

Run 12+ AI agents concurrently. One identity. Full governance. Measurable ROI.

graph TD
    subgraph Intent["THE GREAT GOD OM"]
        O["Human Intent<br/>& Oversight"]
    end

    subgraph Agents["CLAUDE AGENTS (12+)"]
        A["Feature | Bug Fix<br/>Docs | Review"]
    end

    subgraph Verify["GEMINI VERIFICATION"]
        G["LLD Review | Code Review<br/>Security | Quality"]
    end

    subgraph Gov["GOVERNANCE GATES"]
        M["Requirements | Implementation<br/>Reports | Audit Trail"]
    end

    subgraph Future["LANGGRAPH EVOLUTION"]
        R["State Machines<br/>Checkpoints | Supervisors"]
    end

    O --> A
    A --> G
    G --> Gov
    Gov --> R
    R -.->|"Feedback Loop"| A
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Production Evidence: 207 Issues, 159 Closed

AgentOS isn't theoretical. It's been battle-tested through 207 issues (159 closed, 48 open) in 27 days:

View Full Metrics Dashboard →

Issues closed per day (Central Time):
2026-01-11:   2 ##
2026-01-17:   8 ########
2026-01-21:  12 ############
2026-02-01:   7 #######
2026-02-02:  23 #######################
2026-02-03:  55 #######################################################
2026-02-04:  31 ###############################
Theme Examples
Workflow Automation LLD workflow, implementation workflow, TDD workflow
Governance & Gates Gemini verification, mechanical validation, skipped test gates
Bug Fixes Unicode encoding, import errors, stale state bugs
Intelligence Layer Scout workflow, verdict analyzer, template learning
Infrastructure GitHub Actions, Poetry dependencies, cross-platform

Current velocity: 9.4 issues/day average (55 issues on peak day)

The Problem We Solve

AI coding assistants like Claude Code and GitHub Copilot are transforming software development. But enterprise adoption stalls because:

Challenge Reality
No coordination Multiple agents conflict and duplicate work
No governance Security teams can't approve ungoverned AI
No verification AI-generated code goes unreviewed
No metrics Leadership can't prove ROI
Permission friction Constant approval prompts destroy flow state

Organizations run pilots. Developers love the tools. Then adoption plateaus at 10-20% because the infrastructure layer is missing.

The Solution

AgentOS provides that infrastructure layer:

Capability What It Does Enterprise Value
Multi-Agent Orchestration 12+ concurrent agents, one identity Scale without chaos
Gemini Verification AI reviews AI before humans approve Quality gates that work
Governance Gates Enforced checkpoints (design, code, docs) Security team approval
Permission Management Eliminate friction, track patterns Developer productivity
34 Audits Security, privacy, AI safety, compliance Compliance readiness
Metrics & KPIs Adoption, friction, cost, productivity Prove ROI to leadership

For Different Audiences

Engineering Leaders

Architects & Technical Leaders

Developers

Security & Compliance Teams

Core Workflows

AgentOS implements two primary governed workflows:

Requirements Workflow

graph TD
    I["Issue Created"]
    L["Write LLD"]
    G{"Gemini<br/>Review"}
    R["Revise"]
    A["APPROVED"]
    C["Ready for<br/>Implementation"]

    I --> L
    L --> G
    G -->|"BLOCK"| R
    R --> G
    G -->|"APPROVE"| A
    A --> C
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Design documents are reviewed by Gemini before any code is written. Learn more

Implementation Workflow

graph TD
    S["Start Coding"]
    W["Create Worktree"]
    I["Implement"]
    T["Run Tests"]
    R["Generate Reports"]
    G{"Gemini<br/>Review"}
    P["Create PR"]
    M["Merge & Cleanup"]

    S --> W
    W --> I
    I --> T
    T --> R
    R --> G
    G -->|"BLOCK"| I
    G -->|"APPROVE"| P
    P --> M
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Code is reviewed by Gemini before PR creation. Learn more

Roadmap: LangGraph Evolution

AgentOS is production-ready today with prompt-based orchestration. The roadmap transforms it into an enterprise-grade state machine platform:

Timeline Milestone Impact
Q1 2026 LangGraph state machines, checkpointing Gates enforced, not suggested
Q2 2026 Supervisor pattern, LangSmith observability Autonomous task decomposition
Q3 2026 Dynamic tool graphs, multi-tenant support Scale to organizations

See: LangGraph Evolution for the full technical vision.

Key Differentiators

1. Multi-Model Verification (Unique)

Claude builds code. Gemini reviews it. This isn't just "two models" - it's adversarial verification where one AI checks another's work before humans approve. Learn more

2. Friction-First Approach

We obsess over permission friction because it's the #1 adoption killer. Our friction logging protocol (Zugzwang) identifies patterns, and our tools auto-remediate them. Learn more

3. Self-Improving Governance

The system learns from Gemini verdicts to improve templates automatically. 164 verdicts analyzed, 6 template sections added. Learn more

4. Discworld Personas

Every workflow has a Discworld character defining its philosophy. This isn't whimsy - it's intuitive system design. Vimes guards (regression tests), Lu-Tze sweeps (janitor), Brutha remembers (RAG).

The Cast

Persona Function Philosophy
The Great God Om Human Orchestrator Pure Intent
Moist von Lipwig Pipeline Orchestration Keep messages moving
Lord Vetinari Work Visibility Information is power
Commander Vimes Regression Tests Deep suspicion
Captain Angua External Intelligence Sensory awareness
Brutha RAG Memory Perfect recall
Lu-Tze Maintenance Constant sweeping

Full cast →

Get Started

  1. Read the architecture: Multi-Agent Orchestration
  2. Understand the roadmap: LangGraph Evolution
  3. See the metrics: Measuring Productivity
  4. Try it: Quick Start

"A man is not dead while his name is still spoken." GNU Terry Pratchett

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