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Parameterized agent configuration system for multi-agent: Claude Code and Gemini

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AgentOS

Multi-Agent Orchestration Platform for Enterprise AI Development

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

The Headline: Multi-Agent + Multi-Model

graph LR
    subgraph Agents["CLAUDE AGENTS (12+)"]
        A["Feature | Bug Fix | Docs | Review"]
    end

    subgraph Gemini["GEMINI 3 PRO"]
        G["Issue | LLD | Code | Security"]
    end

    subgraph Gov["GOVERNANCE"]
        M["34 Audits | Friction | KPIs"]
    end

    subgraph Road["LANGGRAPH"]
        R["State Machines | Checkpoints"]
    end

    Agents --> Gemini --> Gov --> Road
Loading

What makes AgentOS different:

Capability What It Means
12+ Concurrent Agents Multiple Claude agents work in parallel on features, bugs, docs - all under one user identity
Gemini Reviews Claude Every design doc and code change is reviewed by Gemini 3 Pro before humans see it
Enforced Gates LLD review, implementation review, report generation - gates that can't be skipped
34 Governance Audits OWASP, GDPR, NIST AI Safety - adversarial audits that find violations

The Problem We Solve

AI coding assistants like Claude Code and GitHub Copilot are transforming 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.

AgentOS is that infrastructure layer.

Multi-Agent Orchestration

The headline feature: run 12+ AI agents concurrently under single-user identity with full coordination.

How It Works

Component Function
Single-User Identity All agents share API credentials, git identity, permission patterns
Worktree Isolation Each agent gets its own git worktree - no conflicts, clean PRs
Credential Rotation Automatic rotation across API keys when quota exhausted
Session Coordination Agents can see what others are working on via session logs

Agent Roles

Role Context Tasks
Feature Agent Full codebase New functionality, refactors
Bug Fix Agent Issue-focused Specific bug investigation
Documentation Agent Docs + code README, wiki, API docs
Review Agent PR diff Code review assistance
Audit Agent Compliance Security, privacy audits

Result: One engineer orchestrating 12+ agents can accomplish what previously required a team.

Full Architecture Documentation

Gemini Verification Layer

The key differentiator: Claude builds, Gemini reviews.

This isn't just "two models" - it's adversarial verification where one AI checks another's work before humans approve.

Four Review Gates

Gate When What Gemini Checks
Issue Review Before work starts Requirements clarity, scope, risks
LLD Review Before coding Design completeness, security, testability
Code Review Before PR Quality, patterns, vulnerabilities
Security Audit Before merge OWASP Top 10, dependency risks

Why Multi-Model Matters

Single Model Multi-Model (AgentOS)
Claude reviews Claude's work Gemini reviews Claude's work
Same blind spots Different model catches different mistakes
Trust the output Verify the output
"It looks good to me" Structured JSON verdicts: APPROVE/BLOCK

Model Verification

AgentOS detects silent model downgrades:

  • Gemini CLI sometimes returns Flash when you request Pro
  • Our tools verify the actual model used in the response
  • If downgraded, the review is flagged as invalid

Gemini Architecture Details

Governance Gates

Three mandatory checkpoints that cannot be bypassed:

Issue → LLD Review → Coding → Implementation Review → PR → Report Generation → Merge
         ↑                      ↑                           ↑
    Gemini Gate            Gemini Gate                 Auto-Generated

LLD Review Gate

Before writing ANY code:

  1. Design document submitted to Gemini
  2. Gemini evaluates completeness, security, testability
  3. APPROVE → Proceed to coding
  4. BLOCK → Revise design first

Cost of design fix: 1 hour Cost of code fix: 8 hours Cost of production fix: 80 hours

Implementation Review Gate

Before creating ANY PR:

  1. Implementation report + test report submitted to Gemini
  2. Gemini evaluates quality, coverage, security
  3. APPROVE → Create PR
  4. BLOCK → Fix issues first

Report Generation Gate

Before merge, auto-generate:

  • implementation-report.md - What changed and why
  • test-report.md - Full test output, coverage metrics

Gate Implementation Details

Permission Friction Management

Permission prompts are the #1 adoption killer.

Every "Allow this command?" prompt breaks flow state. Developers either:

  • Click "Allow" without reading (security risk)
  • Get frustrated and stop using the tool (adoption failure)

The Zugzwang Protocol

Real-time friction logging that captures every permission prompt:

# Run in separate terminal during development
poetry run python tools/zugzwang.py

Pattern Detection

AgentOS identifies:

  • Commands that always get approved → Add to allow list
  • Commands that always get denied → Add to deny list
  • Novel patterns → Flag for review

Measurable Reduction

Metric Before After
Prompts per session 15-20 2-3
Time lost to prompts 10+ min < 1 min
Developer frustration High Low

Permission Friction Deep Dive

34 Governance Audits

Audits designed with an adversarial philosophy: they exist to find violations, not confirm compliance.

Audit Categories

Category Count Focus
Security & Privacy 3 OWASP, GDPR, License compliance
AI Governance 7 Bias, Explainability, Safety, Agentic risks
Code Quality 4 Standards, Accessibility, Capabilities
Permission Management 3 Friction, Permissiveness, Self-audit
Documentation Health 6 Reports, LLD alignment, Terminology
Extended 10 Cost, Structure, References, Wiki
Meta 1 Audit system governance

Key Audits

Audit Standard What It Checks
0808 OWASP LLM 2025 AI-specific vulnerabilities
0809 OWASP Agentic 2026 Agent autonomy risks
0810 ISO/IEC 42001 AI management system
0815 Internal Permission friction patterns

Full Audit Catalog

Metrics & KPIs

"How do I prove ROI to leadership?"

Adoption Metrics

Metric What It Shows
Active users / Total engineers Adoption rate
Sessions per user per week Engagement depth
Features shipped with AI assist Productivity impact

Friction Metrics

Metric Target
Permission prompts per session < 3
Time to first productive action < 30 seconds
Session abandonment rate < 5%

Quality Metrics

Metric What It Shows
Gemini first-pass approval rate Design quality
PR revision count Code quality
Post-merge defects Overall quality

Cost Metrics

Metric Calculation
Cost per feature Total API spend / Features shipped
Cost per agent-hour API spend / Active agent hours
ROI (Time saved × Engineer cost) / Platform cost

Full KPI Framework

Roadmap: LangGraph Evolution

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

Phase Timeline Capability Impact
1 Q1 2026 LangGraph state machines Gates structurally enforced
2 Q1 2026 Checkpointing Long tasks survive interruptions
3 Q2 2026 Supervisor pattern Autonomous task decomposition
4 Q2 2026 LangSmith observability Full dashboards, traces, cost attribution
5 Q3 2026 Dynamic tool graphs Context-aware tool selection

Why LangGraph?

Current (Prompt-Based) Future (LangGraph)
Gates enforced by instructions Gates enforced by state machine
Context lost on compaction Context checkpointed to database
Human routes tasks to agents Supervisor auto-routes
Log parsing for metrics LangSmith dashboards

Full Roadmap and Vision

Quick Start

1. Clone and Install

git clone https://github.com/martymcenroe/AgentOS.git
cd AgentOS
poetry install

2. Configure Your Project

mkdir -p YourProject/.claude
cp AgentOS/.claude/project.json.example YourProject/.claude/project.json
# Edit project.json with your project details

3. Generate Configs

poetry run python tools/agentos-generate.py --project YourProject

4. Start Working

The generated configs include:

  • Permission patterns that eliminate friction
  • Slash commands for common operations
  • Hooks for pre/post tool execution

Detailed Setup Guide

Documentation

Wiki

Full documentation at AgentOS Wiki:

Page Description
Multi-Agent Orchestration The headline feature - 12+ concurrent agents
Gemini Verification Multi-model review architecture
LangGraph Evolution Roadmap to enterprise state machines
Governance Gates LLD, implementation, report gates
Permission Friction Eliminating approval prompts
Measuring Productivity KPIs, dashboards, ROI
Security & Compliance OWASP, GDPR, AI Safety
Audits Catalog All 34 governance audits
Tools Reference Python tool documentation
Quick Start 5-minute setup guide

For Different Audiences

Audience Start Here
Engineering Leaders Why AgentOS?
Architects Multi-Agent Orchestration
Security Teams Security & Compliance
Developers Quick Start

About

AgentOS was built by Martin McEnroe, applying 29 years of enterprise technology leadership to the emerging challenge of scaling AI coding assistants across engineering organizations.

Relevant Experience

Role Organization Relevance
Director, Data Science & AI AT&T Led 45-person team, $10M+ annual savings from production AI
VP Product Afiniti AI-powered platform at scale
AI Strategic Consultant TX DOT 76-page enterprise AI strategy

Why This Project Exists

Having led enterprise AI adoption, I know the blockers:

Blocker AgentOS Solution
"Security won't approve ungoverned AI" 34 audits, Gemini gates, enforced checkpoints
"We can't measure productivity" KPI framework, friction tracking, cost attribution
"Agents conflict with each other" Worktree isolation, single-user identity model
"Developers hate the permission prompts" Zugzwang protocol, pattern detection, auto-remediation
"It's just pilots, not real adoption" Infrastructure that scales to organization-wide

Technical Foundation

  • LangChain, LangGraph, LangSmith - Hands-on implementation experience
  • 21 US Patents - Innovation track record
  • CISSP, CCSP, AWS ML Specialty - Security and ML credentials
  • GitHub Copilot, Claude Code - Daily production use

The Bottom Line

This isn't theoretical. It's production infrastructure I use daily to orchestrate 12+ AI agents with full governance.

The code in this repo is the same code that:

  • Runs the Gemini verification gates
  • Tracks permission friction patterns
  • Generates the audit reports
  • Manages credential rotation

If you're scaling AI coding assistants across an engineering organization, this is the infrastructure layer you need.

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MIT

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Parameterized agent configuration system for multi-agent: Claude Code and Gemini

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