ReAct Agent
Intelligent consultation system for e-commerce
Solution Overview
ReAct Agent is an intelligent dialogue system based on Reasoning + Acting technology, designed to automate customer service in e-commerce. The system combines natural language processing capabilities with real-time access to business data.
Market Positioning
Typical AI systems operate in one of two extreme scenarios. Either you get a response that has nothing to do with your question - a template reply that doesn't solve the problem - and you can't easily reach a live operator. Or the opposite: at the slightest deviation from the script, there's an instant transfer to an operator, completely devaluing the bot's presence.
Architectural Solution
ReAct Agent implements a fundamentally different approach:
- Deterministic responses - the system generates answers exclusively based on verified data from the knowledge base. When relevant information is absent - proper escalation instead of generating unreliable content.
- Intelligent routing - automatic determination of operator involvement need based on dialogue context analysis, before customer frustration occurs.
- Maximizing autonomous resolution - full utilization of available toolkit before escalation: searching multiple sources, clarifying details, combining data.
Technical Architecture
Agent Model
Unlike pipeline RAG systems where information is provided to the model in a fixed format, ReAct Agent functions as an autonomous researcher. The system independently determines required data and initiates its retrieval.
When needed, the agent requests the full article from the knowledge base rather than being limited to provided fragments - a capability unavailable in standard RAG systems.
Modular Tool System
The architecture supports unlimited number of tools. The agent autonomously selects the optimal tool depending on the task, for example:
- Semantic search - meaning-based search accounting for synonyms, typos, and different phrasings
- Attribute search - filtering and sorting by price, category, characteristics
- CRM integration - access to order data, statuses, customer history
- Full-text access - retrieving complete articles from knowledge base
Request Processing Cycle (ReAct)
ReAct (Reasoning + Acting) technology provides an iterative processing workflow:
- Reasoning - request analysis and hypothesis formation about necessary actions
- Acting - execution of selected tool with corresponding parameters
- Observation - result analysis and decision about next step: continue searching or form final response
Security System
Each request passes through a five-level security perimeter:
Caching System
Three-level caching architecture provides up to 95% savings on inference costs:
Verified Cache - manually verified responses, 20-30% hit rate.
Semantic Cache - automatic by semantic similarity, 60-80% hit rate.
Exact Match Cache - exact request match, 5-10% hit rate.
Key feature: cache considers dialogue context. Identical requests in different contexts receive corresponding responses from different cache entries.
Semantic Search
The system uses 1024-dimensional vector representations (embeddings) for meaning-based search:
Query vectorization - text conversion to numerical vector, "semantic fingerprint".
Nearest neighbor search - identification of 30 closest documents by cosine distance.
Reranking - re-ranking and selection of 8 most relevant results.
Response generation - forming response based on selected context.
Performance and Economics
Cost of processing 1000 requests: without caching ~$5.70, with 80% hit rate ~$1.14 (80% savings).
Key Advantages
- Determinism - responses exclusively based on verified data
- Intelligent escalation - automatic operator necessity detection
- Modularity - unlimited number of integrable tools
- Performance - 60-80% of responses in fractions of a second
- Security - five-level protection system
- Cost efficiency - up to 95% savings on inference
- Fault tolerance - automatic recovery and backup models