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.
Example:: 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