multiagents-sale-team

Inspiration

What it does

How we built itInspiration

Enterprise sales and support teams struggle with workload capacity, customer experience consistency, and response times across the entire customer journey. We envisioned intelligent agents handling the complete lifecycle: reception, technical qualification, deal negotiation and closure, and technical support—freeing human teams to focus on complex, high-value relationships and strategic accounts. What It Does The multi-agent system consists of four specialized agents working together seamlessly: Customer Receptionist Agent: First-line interaction, intelligent routing, and personalized welcome to enhance customer experience Sales Engineer Agent: Technical qualification, solution configuration, and pre-sales support to accelerate sales cycles Account Executive Agent: Deal finalization, negotiation, pricing optimization, and contract closure to drive revenue growth (NEW) Tech Support Agent: Tier 1 issue resolution, knowledge base querying, and intelligent ticket escalation for customer success All agents are powered by Google GenAI LLM models with the Google ADK agents framework, enhanced with ElevenLabs voice for natural interactions, deployed securely on Daytona, reviewed with CodeRabbit MCP, and continuously monitored through Galileo observability. How We Built It We engineered a comprehensive technology stack combining: Intelligence Layer: Google GenAI LLM models for sharp reasoning and Google ADK agents framework for orchestration and multi-agent coordination Voice Layer: ElevenLabs for hyper-realistic, low-latency voice interactions that build customer trust Deployment Layer: Daytona for secure, rapid environment creation (90ms) with stateful operations and enterprise-grade security Quality Assurance Layer: CodeRabbit MCP for automated code reviews focusing on agentic logic, vulnerability detection, and code quality Observability Layer: Galileo for end-to-end tracing, debugging non-determinism, guardrails evaluation, and operational insights The architecture ensures sharp reasoning, independent decision-making, safe tool integration, and measurable real-world impact across all agents. Challenges We Ran Into Building reliable, non-deterministic LLM-powered agents at scale — Managing the inherent unpredictability of large language models while maintaining consistency across multiple specialized agents Ensuring security and safe execution in production environments — Creating isolated, ephemeral execution environments that mitigate risks of untrusted code and protect customer data Achieving end-to-end observability and debugging agent behavior — Tracing complex decision-making chains across multiple agents and identifying failure points in multi-agent systems Integrating voice interactions with complex reasoning and negotiation workflows — Achieving low-latency, expressive voice responses while executing sophisticated multi-step tasks including deal negotiation

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for multiagents-sale-team

Built With

• coderabbit
• daytona
• elevenlabs
• google-genai