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Autonomous Conversational Agent

Aura AI Support Hub

An autonomous support agent utilizing LangGraph state machines, Pinecone RAG search, and human-in-the-loop checks.

AuthenticationAPI IntegrationAI EnabledProduction ReadySecurity Best Practices
Industry: Customer Experience
Primary Outcome: 64%
ai-customer-support-agent.doavelon.comAura AI ControlsTRANSACTION INDEXLATENCY INDEX14.8 msSYSTEM READY: Ingestion endpoints initialized successfully.GET /telemetry/stream - 200 OKPOST /order/create - pending check...

Executive Overview

An autonomous support agent utilizing LangGraph state machines, Pinecone RAG search, and human-in-the-loop checks.

Business Problem

A support desk faced high ticket volumes, leading to long response times and increased support costs.

Client Goals

Automate resolution for common support tickets.
Ground agent responses in private company documentation.
Reduce ticket response times and support costs.

Technical Challenges

Avoiding agent hallucinations and managing chat state cycles while ensuring fast responses.

Solution

Designed and deployed an autonomous support agent. Built a cyclic LangGraph state machine. Integrated semantic search to ground responses in support docs.

Architecture Overview

Chat clients connect to a Python middleware API. LangGraph manages conversation state, querying Pinecone for context and invoking LLM endpoints.

Client ClientEdge GatewayGraphQL RouterSession CacheShopify CoreStorefront API

Core Features

1
Cyclic conversational state routers.
2
Semantic document retrieval engines.
3
Human-in-the-loop validation checkpoints.
4
Interactive support team dashboards.

Technology Stack

LangGraphPythonFastAPIPineconeOpenAI APIPostgreSQLRedis

API Integrations

OpenAI Inference APIZendesk Ticket Synchronization APISlack Support Notification webhook APIPinecone Vector Indexing API

Database Design

Pinecone stores vector embeddings. PostgreSQL manages active chat sessions and human logs.

Authentication Strategy

Session verification via secure cookies, with JWT authentication for administrative consoles.

Security Considerations

Enforces strict data access rules, filtering document search results by tenant identifier.

Performance Optimizations

Caches common query vectors in Redis, reducing vector database loads.

Testing Strategy

Unit tests run via pytest. Automated checks run queries against test documents to evaluate accuracy.

Continuous Integration Pipeline verified with automated test suites on deployment hooks.

Deployment Strategy

Deploys as secure Docker containers to AWS ECS, using AWS Fargate serverless infrastructure.

Monitoring Strategy

LangSmith tracks agent paths and accuracy, alerting managers to anomalous responses.

Business Results & Lessons

Automated common support tickets, reduced response times, and lowered support costs.

Lessons Learned

Max-retry limits prevent agent loops. Human-in-the-loop gates are critical for high-risk actions.

Future Improvements Roadmap

Integrate multi-language translation tools. • Deploy sentiment analysis routing.

Measurable Metrics

64%First-Contact Automation
< 900msAverage Response Speed
-40%Support Operations Cost

Case Study FAQ

How does the agent avoid hallucinating details?
↓
Responses are grounded in verified documentation retrieved via semantic search. Responses are blocked if query match scores fall below thresholds.
Can the agent perform account actions?
↓
Yes, but high-risk actions (like processing refunds) are paused for human approval.

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