Real-time Voice Assistant for Market Intelligence
Develop a sophisticated real-time voice assistant capable of performing competitive market intelligence and product analysis. This challenge focuses on building a responsive, multi-modal agent system that leverages the AI SDK for seamless streaming interactions and sophisticated tool orchestration. The core reasoning will be powered by Google's Gemini 3 Flash, providing rapid and accurate insights based on voice input. The system will integrate Microsoft AutoGen to spin up specialized, scriptable agents that handle deep-dive research tasks, collaborating with the primary voice agent. Security and data integrity are paramount, so LatticeFlow AI will be utilized to implement robust model safety policies and secure data pipelines. For comprehensive monitoring and evaluation, LangFuse will be integrated to trace agent interactions and performance metrics, ensuring the system operates efficiently and reliably. Retell AI will provide the real-time voice-to-text and text-to-speech capabilities, enabling natural language interactions for the end-user. This challenge emphasizes cutting-edge multi-agent orchestration, real-time voice processing, and advanced AI engineering practices to deliver a high-performance, secure, and observable AI application.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a sophisticated real-time voice assistant capable of performing competitive market intelligence and product analysis. This challenge focuses on building a responsive, multi-modal agent system that leverages the AI SDK for seamless streaming interactions and sophisticated tool orchestration. The core reasoning will be powered by Google's Gemini 3 Flash, providing rapid and accurate insights based on voice input. The system will integrate Microsoft AutoGen to spin up specialized, scriptable agents that handle deep-dive research tasks, collaborating with the primary voice agent. Security and data integrity are paramount, so LatticeFlow AI will be utilized to implement robust model safety policies and secure data pipelines. For comprehensive monitoring and evaluation, LangFuse will be integrated to trace agent interactions and performance metrics, ensuring the system operates efficiently and reliably. Retell AI will provide the real-time voice-to-text and text-to-speech capabilities, enabling natural language interactions for the end-user. This challenge emphasizes cutting-edge multi-agent orchestration, real-time voice processing, and advanced AI engineering practices to deliver a high-performance, secure, and observable AI application.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.
Real-time Voice Latency
Ensure average response time from audio input to audio output is under 3 seconds.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Market Analysis Accuracy
Verify key findings from market analysis queries are factually correct and relevant.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
AutoGen Task Completion
Confirm AutoGen agents successfully complete their assigned sub-tasks.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Security Policy Adherence
Check if LatticeFlow AI correctly identifies and mitigates policy violations.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Semantic Similarity Score (Analysis)
Cosine similarity between generated analysis text and expert-reviewed benchmark text. • target: 0.85 • range: 0.7-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Tool Invocation Rate
Percentage of relevant tools invoked per complex query. • target: 0.95 • range: 0.8-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
LangFuse Trace Completeness
Percentage of agent steps and tool calls captured in LangFuse traces. • target: 1 • range: 0.95-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master Vercel's AI SDK for building streaming AI applications in TypeScript, including advanced tool calling and state management.
Implement real-time voice input and output using Retell AI, connecting it seamlessly with the AI SDK and underlying language models.
Orchestrate dynamic agent teams with Microsoft AutoGen, defining specialized roles for information gathering, synthesis, and summarization.
Design secure agent-to-agent communication protocols and data handling practices, integrating LatticeFlow AI for compliance and risk management.
Build extended reasoning pipelines leveraging Gemini 3 Flash for rapid and context-aware analysis of complex market data.
Integrate LangFuse into the AI SDK and AutoGen workflows for end-to-end tracing, monitoring, and performance evaluation of agent interactions.
Develop custom tools for the AI SDK agents to access external market data APIs, web scrapers, and internal knowledge bases.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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