Legal Agent Orchestrator: A Multi-Agent Collaborative Legal AI Workflow System

This system generates auditable legal opinions through collaboration among 8 independent Claude Code professional legal agents. Each agent has an independent jurisdiction, knowledge base, and MCP tools, enabling true multi-expert collaborative reasoning and breaking through the knowledge mixing limitations of single-LLM legal AI. Core features include physical isolation of agents, flexible collaboration modes, complete audit trails, and an efficient context architecture.

Most current legal AI products are based on a single LLM. Knowledge mixing leads to ambiguous outputs across jurisdictions (e.g., GDPR and PIPA). The core insight of this system is: true multi-expert reasoning requires physically isolated agents, not role-playing by a single model.

The system consists of a central orchestrator and 8 professional agents. The orchestrator is responsible for case intake (generating CASE_ID and event logs), intelligent classification (jurisdiction/domain/task type), agent assignment, and result assembly (deliverables include legal opinions, audit logs, etc.). The 8 agents are independent GitHub repositories, each with exclusive system prompts, skill files, knowledge bases, and a 200K context window, covering areas such as cross-border research, legal drafting, and GDPR/PIPA expertise.

1. Sequential Handover: Suitable for single-domain cases (research → drafting → review, Phase1 validated); 2. Parallel Research Merging: Suitable for cross-domain non-controversial cases (e.g., PIPA+GDPR parallel → drafting → review, Phase2.2 validated); 3. Multi-round Debate: Suitable for complex cross-jurisdictional controversial cases (expert rebuttal → ruling drafting → review, Phase2.3 under development).

• Context Efficiency: The orchestrator uses only 25-40K tokens, while each agent has a full 200K context window, avoiding shared context bloat; - Complete Audit Trail: events.jsonl records all steps (agent assignment, source citations, fact-checking, etc.); - Quality First: A single case consumes over 200K tokens, trading cost for high-quality defensible legal opinions.

1. Intake: Generate CASE_ID and initialize logs; 2. Classification: Identify Korea + game industry + data privacy + EU GDPR; 3. Parallel Assignment: Game legal research, PIPA expert, GDPR expert; 4. Merge Results: Identify legal intersections and conflicts;5. Drafting: Write in the format of a Korean legal memorandum;6. Review: Validate sources (law.go.kr, eur-lex, etc.);7. Delivery: Assemble the final package.

Limitations: Dependence on Claude Code runtime (core functions retained), long processing time (trade-off between quality and speed), multi-round debate mode to be improved. Future Directions: Intelligent agent selection algorithm, automated quality assessment, integration of more legal databases, addition of agents for new jurisdictions.