Case Studies

Government Legal Intelligent Agent Platform

Large Government Legal Review Agency

Built a gov-legal agent platform based on FIM Agent, breaking through LLM long-text "forgetting" issues to achieve intelligent pre-review and risk control for complex legal texts.

AI
Key Metrics

Project Results

0%
Efficiency Increase
Average processing time per case file significantly reduced
0%
Context Integrity
Ensuring no key constraints are missed in long-case reasoning
0%
Expert Adoption
Percentage of AI-generated suggestions accepted by legal experts
Full
Traceability
Full evidence chain provided for every reasoning step
Core Technology Features

Technical Highlights

FIM Agent Dual-Track

Workflow ensures compliance while Planner handles autonomous judgment, deeply fitting complex scenarios

Verbatim Grounding

AI conclusions are strictly anchored to original text clauses for full traceability, addressing the "forgetting" issue

Intelligent Provenance

Problem-Basis-Logic-Conclusion: full chain explainability, rejecting black-box operations

Three-layer Knowledge Arch

Global/Org/Tenant isolation, achieving "default isolation, on-demand sharing"

Full100+AI1000+
Project Overview

Client Background

This large government legal agency handles a massive volume of complex legal texts and compliance documents. The traditional manual mode faced challenges of long case files and multiple compliance dimensions (hundreds of rules). Early attempts with generic LLMs were limited by context windows and hallucination issues, failing to handle extra-long files with the necessary explainability for rigorous compliance.

Technology Stack

FIM AgentContext EngineeringKnowledge GraphRAG Architecture
Transformation

From Challenges to Solutions

Transformation
1Long-text information decay: Complex cases often span hundreds of pages, and models tend to forget key constraints after multiple turns, leading to contradictory conclusions
Developed FIM Agent framework using a "Workflow (deterministic) + Planner (autonomous)" hybrid scheduling to balance compliance and flexibility
2Black-box reasoning distrust: End-to-end generative AI lacks intermediate reasoning processes, making it hard for experts to judge the basis of conclusions
Self-developed Context Engineering Engine with "Verbatim Grounding" strategy to solve recursive information decay and ensure long-chain reasoning consistency
3Knowledge permission management: Different departments have both shared standards and internal rules, making it difficult for traditional knowledge bases to isolate data
Built Multi-tenant Three-layer Knowledge Architecture for "Global Shared - Org Private - Tenant Isolated" management with automatic inheritance
4Inflexible workflows: Simple workflows cannot handle complex logical judgments, while pure Agent planning can easily spiral out of control
Created Intelligent Provenance Evidence Chain outputting "Problem - Compliance Basis - Logic - Conclusion" loop to significantly improve expert adoption
Technical Architecture

System Architecture Design

Layer 1
Knowledge Service Layer

Innovative 3-tier knowledge architecture (Global/Org/Tenant) enabling 'default isolation, scalable sharing'

Global SharedOrg PrivateTenant IsolationInheritance
Layer 2
Agent Synergy Layer (Agent SDK)

Context-driven Agent SDK supporting dual-track scheduling: Workflow (Deterministic) + Planner (Autonomous)

Dual-TrackContext EngVerbatim GroundingHot-Swap Skills
Layer 3
Intelligent Application Layer

Business-oriented Agent Matrix covering intelligent pre-check and full lifecycle risk control of complex legal texts

Normative Pre-checkDeep Risk AnalysisTerm ConsistencyTraceability
复杂文档入湖Agent 自主协同长文本研判对标Verbatim Grounding
Implementation Timeline

Phased Implementation

1
Phase 1

Core Foundation Building

Completed FIM Agent framework development, established three-layer knowledge architecture, and built high-quality legal evaluation datasets

2
Phase 2

Agent Swarm Development

Developed agents for regulatory pre-review, deep risk control, and clause consistency; optimized RAG retrieval performance

3
Phase 3

Demonstration & Scaling

Deployed at relevant agencies and subordinate units; expert evaluation showed significant overall processing efficiency improvements

Testimonial

Client Testimonial

Previously, reviewing a complex case took days of cross-referencing hundreds of clauses. Now, the AI agent provides a complete reasoning chain with verbatim grounding, reducing our workload significantly while maintaining rigor.

Legal Review Division Director

Senior Legal Expert

FAQ

Frequently Asked Questions

How does the system solve the "forgetting" problem in long texts?
Can we trust the AI's legal conclusions?
How are internal rules and public standards separated?
Does the system support complex logic or just simple search?

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