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Antigravity Agent
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id: wiki-2026-0508-다수-팀-협업-환경
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title: 다수 팀 협업 환경
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Multi-team Collaboration, Multi-Team AI Workflow, Cross-team AI Coordination]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [collaboration, multi-team, ai-workflow, ml-platform]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: mlflow-langfuse-argo
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---
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# 다수 팀 협업 환경
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## 매 한 줄
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> **"매 multi-team AI 환경은 shared model registry + isolated namespace + cross-team observability 의 triad"**. 2024년 단일 ML team 시대가 끝나고, 2026년 enterprise 의 매 product team 마다 LLM/agent 를 owning. Shared infra (model registry, eval harness, prompt store) 위에 team-isolated workspace (Argo namespace, Langfuse project) 를 결합.
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## 매 핵심
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### 매 conflict 영역
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- **model versioning**: team A 가 fine-tune 한 Llama-3.3 70B 를 team B 도 쓰는데 update 시 regression.
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- **prompt drift**: 동일 base prompt 가 team 마다 fork 되어 7가지 variant 공존.
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- **eval inconsistency**: team 마다 다른 eval set → 비교 불가.
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- **GPU contention**: H200 cluster 의 fair-share scheduling 부재 시 noisy neighbor.
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### 매 governance layer
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- **Model Registry (MLflow / Weights & Biases)**: canonical model card, semver tag, deprecation policy.
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- **Prompt Store (Langfuse / Humanloop)**: versioned prompts, A/B winner promotion.
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- **Eval Harness (Inspect AI / promptfoo)**: shared regression suite — 매 model bump 시 자동 trigger.
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- **Observability (Langfuse + OpenTelemetry)**: 매 team project 분리, leadership level 의 cross-team dashboard.
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### 매 응용
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1. Platform team 이 base infrastructure 제공, product team 이 application layer 구축.
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2. AI Center of Excellence — 매 quarterly review of cross-team incidents.
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3. RACI matrix — model owner / prompt owner / eval owner 명시.
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## 💻 패턴
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### MLflow shared registry — team isolation via aliases
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```python
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import mlflow
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from mlflow import MlflowClient
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client = MlflowClient(tracking_uri="https://mlflow.corp/")
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# Platform team registers canonical model
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mv = client.create_model_version(
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name="llama-3.3-70b-instruct-finetuned",
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source="s3://corp-models/llama33-v4/",
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description="Q2 2026 finetune; eval set v3.2",
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)
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client.set_registered_model_alias(
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name="llama-3.3-70b-instruct-finetuned",
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alias="prod-team-search",
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version=mv.version,
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)
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client.set_registered_model_alias(
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name="llama-3.3-70b-instruct-finetuned",
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alias="prod-team-support",
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version=mv.version,
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)
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# Each team pins its own alias → independent rollout cadence
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```
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### Langfuse multi-project prompt versioning
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```python
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from langfuse import Langfuse
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lf = Langfuse(public_key=PK, secret_key=SK, host="https://langfuse.corp")
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# Team A creates a prompt
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lf.create_prompt(
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name="search/intent-classifier",
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prompt="You classify user search intent. Categories: {{categories}}.",
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labels=["production"], # auto-promoted version label
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config={"model": "claude-opus-4-7", "temperature": 0.0},
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)
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# Team B compiles the same prompt (linked, not copied)
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prompt = lf.get_prompt("search/intent-classifier", label="production")
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compiled = prompt.compile(categories="navigational, informational, transactional")
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```
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### Argo Workflows — team-namespaced GPU jobs with priority class
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```yaml
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apiVersion: argoproj.io/v1alpha1
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kind: Workflow
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metadata:
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generateName: finetune-team-search-
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namespace: ai-team-search
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spec:
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entrypoint: train
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podGC: { strategy: OnWorkflowSuccess }
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templates:
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- name: train
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priorityClassName: team-search-prod # fair-share scheduling
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container:
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image: corp-registry/finetune:cuda12.6
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resources:
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limits: { nvidia.com/gpu: 8, memory: 1Ti }
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env:
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- name: MLFLOW_TRACKING_URI
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value: https://mlflow.corp
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- name: WANDB_PROJECT
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value: team-search
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```
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### Cross-team eval harness with Inspect AI
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```python
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from inspect_ai import eval_async, Task, task
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from inspect_ai.dataset import json_dataset
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from inspect_ai.scorer import model_graded_qa
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@task
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def shared_safety_suite():
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return Task(
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dataset=json_dataset("s3://corp-evals/safety-v3.2.jsonl"),
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scorer=model_graded_qa(model="claude-opus-4-7"),
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)
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# Run across all team-owned models nightly
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models = [
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"team-search/llama-3.3-70b@prod",
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"team-support/llama-3.3-70b@prod",
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"team-rec/llama-3.3-70b@prod",
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]
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results = await eval_async(shared_safety_suite, model=models)
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# Publish to shared dashboard; alert if any team regresses >2% vs last week
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```
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### OPA policy gate for model promotion
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```rego
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package modelregistry.promotion
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deny[msg] {
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input.action == "promote"
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input.target_alias == "prod"
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not input.eval_results.safety_pass_rate >= 0.95
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msg := sprintf("safety_pass_rate=%.3f below 0.95", [input.eval_results.safety_pass_rate])
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}
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deny[msg] {
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input.action == "promote"
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not input.has_owner_approval
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msg := "missing model_owner approval"
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}
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```
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### Cross-team incident postmortem template (YAML, version-controlled)
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```yaml
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incident_id: 2026-Q2-013
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date: 2026-05-08
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owning_team: team-search
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affected_teams: [team-support, team-rec]
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root_cause: |
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team-search rolled new finetune to alias=prod without notifying
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downstream consumers; intent-classifier prompt assumed older format.
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detection: Langfuse anomaly (latency p95 spike) — 14 min
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resolution: rolled back model alias; published deprecation policy
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action_items:
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- owner: platform-team
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due: 2026-05-22
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task: enforce 7-day deprecation notice via OPA
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| 2-3 teams, single product | shared monorepo + single MLflow project |
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| 5-15 teams, mixed maturity | platform team + per-team namespace |
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| 15+ teams, enterprise | full governance layer + AI CoE + OPA gates |
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| Regulated (finance/health) | add audit log + immutable model lineage |
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**기본값**: MLflow registry + Langfuse prompt store + Argo namespace per team + shared Inspect AI eval suite.
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## 🔗 Graph
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- 응용: [[Model_Registry]] · [[Large_Frontend_Projects]]
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- Adjacent: [[Iterative Prompting]] · [[Parameter]]
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## 🤖 LLM 활용
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**언제**: 매 enterprise 의 5+ teams 가 LLM/agent product 를 ship 할 때, shared eval/registry 가 미존재할 때.
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**언제 X**: 매 single team / single model — 매 over-engineering. Notion + GitHub 면 충분.
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## ❌ 안티패턴
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- **Shadow IT model**: team 이 platform 우회하여 personal HF token 으로 model serving — security/cost blind spot.
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- **Prompt copy-paste**: Slack 으로 prompt 공유 → drift, no versioning.
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- **Eval set fragmentation**: team 마다 자체 eval → cross-team comparison 불가.
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- **No deprecation policy**: alias=prod 의 silent breaking change.
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- **Single GPU pool, no priority class**: noisy neighbor 가 매 production inference 를 starvation.
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## 🧪 검증 / 중복
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- Verified (MLflow 2.x docs, Langfuse v3 multi-project, Argo Workflows fair-share scheduling, Inspect AI 0.3+).
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- 신뢰도 A — 매 production-grade enterprise pattern.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — multi-team AI governance triad + 6 patterns |
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