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AI Team OS

Your AI coding tool stops when you stop prompting. Ours doesn't.

AI Team OS turns Claude Code into a self-driving AI company. You're the Chairman. AI is the CEO. Set the vision — the system executes, learns, and evolves autonomously.

The Problem With Every Other AI Tool

Every AI coding assistant works the same way: you prompt, it responds, it stops. The moment you step away, work stops. You come back to a blank prompt.

AI Team OS works differently.

You walk away at night. The next morning you open your laptop and find:

• The CEO checked the task wall, picked up the next highest-priority item, and shipped it
• When it hit a blocker that needed your approval, it parked that thread and switched to a parallel workstream
• R&D agents scanned three competitor frameworks and found a technique worth adopting
• A brainstorming meeting was organized, 5 agents debated 4 proposals, and the best one was put on the task wall

You didn't prompt any of that. The system just ran.

How It Works

You're the Chairman. The AI Leader is the CEO.

The CEO doesn't wait for instructions. It checks the task wall, picks the highest-priority item, assigns the right specialist Agent, and drives execution. When blocked, it switches workstreams. When all planned work is done, R&D agents activate — scanning for new technologies, organizing brainstorming meetings, and feeding improvements back into the system.

Every failure makes the system smarter. "Failure Alchemy" extracts defensive rules, generates training cases for future Agents, and submits improvement proposals — the system develops antibodies against its own mistakes.

Core Capabilities

1. Autonomous Operation

The CEO never idles. It continuously advances work based on task wall priorities:

• Checks the task wall for next highest-priority item when a task completes
• When blocked on something requiring your approval, parks that thread and switches to parallel workstreams
• Batches all strategic questions and reports them when you return — no interruptions for tactical decisions
• Deadlock detection: if the loop stalls, it surfaces the blocker rather than spinning

2. Self-Improvement

The system doesn't just execute — it evolves:

• R&D cycle: Research agents scan competitors, new frameworks, and community tools. Findings go to brainstorming meetings where agents challenge each other. Conclusions become implementation plans on the task wall.
• Failure Alchemy: Every failed task triggers root cause extraction, classification, and three outputs:
  • Antibody — failure stored in team memory to prevent the same mistake
  • Vaccine — high-frequency failure patterns converted into pre-task warnings
  • Catalyst — analysis injected into Agent system prompts to improve future execution

3. Team Collaboration

Not a single Agent. A structured organization:

• 25 professional Agent templates (23 base + 2 debate roles) with recommendation engine — Engineering, Testing, Research, Management — ready out of the box
• 8 structured meeting templates with keyword-based auto-select, built on Six Thinking Hats, DACI, and Design Sprint methodologies
• Department grouping — Engineering / QA / Research with cross-team coordination
• Every meeting produces actionable conclusions. "We discussed but didn't decide" is not an outcome.

4. Full Transparency

Nothing is a black box:

• Decision Cockpit: event stream + decision timeline + intent inspection — every decision has a traceable record
• Activity Tracking: real-time status of every Agent and what it's working on
• What-If Analyzer: compare multiple approaches before committing, with path simulation and recommendations

5. Workflow Pipeline Orchestration

Every task follows a structured, enforced workflow — no more ad-hoc execution:

• 7 pipeline templates: feature (Research→Design→Implement→Review→Test→Deploy), bugfix, research, refactor, quick-fix, spike, hotfix
• Auto-attach via task_type: pass task_type="feature" to task_create and the pipeline mounts automatically
• Progressive enforcement: hook detects tasks without pipelines — soft reminder → strong reminder → hard block (exit 2) on third occurrence
• Auto phase progression: each stage recommends the right Agent template; pipeline_advance moves to next stage automatically
• Lightest escape hatch: quick-fix (Implement→Test only) for truly trivial changes
• Channel communication: team: / project: / global channels with @mention support
• Debate mode: 4-round structured debate (Advocate→Critic→Response→Judge) via debate_start / debate_code_review
• Git automation: git_auto_commit / git_create_pr / git_status_check for streamlined version control
• Semantic cache: BM25 + Jaccard similarity matching with JSON persistence and TTL expiry
• Execution pattern memory: success/failure pattern recording + BM25 retrieval + subagent context injection

6. Safety & Behavioral Enforcement

Built-in guardrails so the system can run unsupervised without surprises:

• Guardrails L1: 7 dangerous pattern detections + PII warnings + InputGuardrailMiddleware
• Local agent blocking: all non-readonly agents must declare team_name/name — prevents rogue background agents
• S1 safety rules: regex-based scan catches destructive commands (rm -rf, force push, hardcoded secrets) including uppercase flags and heredoc patterns
• 4-layer defense rule system: 48+ rules covering workflow, delegation, session, and safety layers
• File lock / workspace isolation: acquire/release/check/list + TTL=300s + hook warnings to prevent concurrent edits
• Agent trust scoring: trust_score (0-1) auto-adjusts on task success/failure, weighted into auto_assign
• Agent Watchdog heartbeat: agent_heartbeat / watchdog_check with 5-min TTL — detects stalled or crashed agents automatically
• SRE error budget model: GREEN/YELLOW/ORANGE/RED 4-level response with sliding window (20 tasks), error_budget_status / error_budget_update tools
• Completion verification: verify_completion checks task status + memo existence — prevents hallucinated "done" reports
• Ecosystem integration recipes: 4 preset recipes (GitHub / Slack / Linear / Full-stack team) via ecosystem_recipes() tool
• find_skill 3-layer progressive discovery: quick recommend → category browse → full detail, reducing tool-call overhead

7. Zero Extra Cost

Runs entirely within your existing Claude Code subscription:

• No external API calls, no extra token spend
• MCP tools, hooks, and Agent templates are all local
• 100% utilization of your CC plan

8. Ecosystem Research Platform (progressive funnel in v1.5.0)

A project-isolated knowledge base that accumulates research findings over time. Each repo progresses through 4 stages, with token-efficient triggers and append-only history:

• Stage 0 — Auto shallow-summary on archive: newly-archived repos automatically get a 200-400 char ai-engineer summary (core function / positioning / advantages). 8-class failure handling with self-learning (3+ same-class fails → pattern_record, future agents read lessons via pattern_search). Worker auto-revives deleted/private repos when GitHub returns 200 again.
• Stage 1 — On-demand architecture analysis: user picks research direction ("memory_system") → batch-dispatch backend-architect agents to read architecture key files
• Stage 2 — Multi-perspective debate: triggers existing debate_start (NOT a built-in debate engine — reuses meeting system). Meeting → ecosystem reverse-writeback hook reminds Leader to record verdicts back to deep_review
• Stage 3 — Reference / Integrate marking: mark_as_reference adds tag for future quick recall (avoid re-deep-scanning); start_integration triggers existing task_create for actual implementation
• Project-customizable thresholds: each project sets min_stars / top_n / refresh_interval_days / focus_topics. AI Team OS default: stars ≥ 5K, top 200, focus on claude-code / mcp / agent-framework
• Active vs Full dual-view: data is append-only forever. Stars-falling repos kept (just is_active=False); stars climbing back auto-promotes + re-queues Stage 0
• Dashboard /ecosystem: list with stage badges + research timeline + candidate-filter page (/ecosystem/research) + per-project settings tab
• 30+ MCP tools / 15+ REST endpoints / SQLite append-only history snapshots

It Built Itself

AI Team OS managed its own development:

• Organized 5 innovation brainstorming meetings with multi-agent debate
• Conducted competitive analysis across CrewAI, AutoGen, LangGraph, and Devin
• Shipped 67 tasks across 5 major innovation features
• Generated 14 design documents totaling 10,000+ lines

The system that builds your projects... built itself.

How It Compares