Enterprise-Grade Intelligent Agent Construction Guide: Practical Implementation Based on LLM and Proprietary Knowledge Base

1. Three Evolutionary Paths of Knowledge Base Architecture
   Through 18 months of industrial deployment testing (2,000+ technical documents), we validated three knowledge base architectures:

1.1 Pure Vector Retrieval (RAG)

• Embedding-based document vectorization with ANN search
• 92% accuracy for single-document queries (e.g., transformer oil temperature handling)
• 1.2s response time but only 58% recall for cross-document reasoning (e.g., reclosure failure analysis)
• Limitation: Key information scattered across tables/flowcharts

1.2 Graph-Enhanced Retrieval (GraphRAG)

• Multi-hop reasoning via knowledge graph construction
• Improved cross-document recall to 83% but faced three challenges:
  • Entity recognition requiring >99% accuracy
  • Custom regex-based relation extraction engine
  • Daily full reconstruction despite incremental updates

1.3 Structured Knowledge Compilation

• Markdown-based structured knowledge compilation inspired by Wikipedia
• 91% reasoning accuracy for complex queries but with:
  • High maintenance cost requiring domain experts
  • Context window limitation (8K tokens for 35B model with FP8 quantization)

Production Deployment: Hybrid architecture combining RAG (<1.5s response for simple queries), GraphRAG (>85% accuracy for complex reasoning), and manually maintained structured Wiki for core operations knowledge.

1. Four Pillars of Intelligent Agent Engineering
   2.1 LLM Selection Criteria
   Three-dimensional evaluation framework for power plant duty assistant systems:

• Hardware: 35B model with TP=2 on dual A100 (120 tokens/s throughput after FP8 quantization)
• Business: 95% queries answered within 3s with source citations
• Cost: <$0.003 per inference (¥0.02 conversion)
• Performance: Qwen3.6-35B showed 17% higher accuracy in technical term recognition

2.2 Toolchain Design Principles

• Strict JSON Schema validation (blocked 42% abnormal calls)
• Principle of least privilege (e.g., knowledge search tool only exposes /search endpoint)
• Anti-hallucination mechanism (auto-review for responses with confidence <0.7)
• Case Study: Eliminated phantom /get_weather API through tool whitelisting

2.3 Memory Optimization
Hierarchical memory system:

• Short-term: Redis storing last 100 dialogues (1h TTL)
• Long-term: Vector database with HNSW indexing for critical knowledge
• Reflection mechanism: Auto-generated logs with KB update suggestions
• Result: 23% accuracy improvement with <12GB memory usage

1. Production Deployment Essentials
   3.1 Performance Optimization

• Quantization: 4-bit GPTQ compression (<2% accuracy loss for 35B model)
• Batching: Dynamic adjustment (batch=8 during idle, batch=2 at peak)
• Caching: Two-level (memory+SSD) caching with 67% hit rate

3.2 Monitoring Framework
Four-dimensional metrics:

• Timeliness: P99<3s with auto-degradation
• Quality: >90% answer citation rate, <5% manual review rate
• Resources: 60-80% GPU utilization target
• Anomaly: LSTM-based overload prediction (15min advance warning)

3.3 Disaster Recovery
Active-active architecture:

• Primary: Local datacenter handling 95% requests
• Secondary: Cloud backup with 30s failover via dedicated line
• Offline mode: Domestic model fallback during network outage

1. Future Development Directions
   4.1 Multimodal Knowledge Processing
   Developing image-text joint encoding with visual Transformers, showing 19% improvement in fault diagnosis accuracy through equipment monitoring screenshot analysis.

4.2 Autonomous Evolution
Feedback loop system converting user corrections into training samples, enabling 5x faster knowledge updates through weekly model fine-tuning.

4.3 Edge Deployment
Exploring 8B model deployment on industrial PCs with knowledge distillation (2GB memory footprint) for substation edge scenarios.

Conclusion: Building intelligent agents requires balancing technical sophistication with engineering practicality. We recommend starting with RAG, gradually incorporating graph enhancement, and evolving toward autonomous architectures. Implement comprehensive monitoring and conduct continuous AB testing. Remember: There’s no universal best solution - only optimal technology combinations for specific business scenarios.