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Build Proactive Personalized Assistant with AutoGen & Gemini 2.5 Pro

Inspired by Apple's shift towards a personalized, Gemini-powered Siri, this challenge tasks you with building a sophisticated multi-agent system using Microsoft's AutoGen framework. The goal is to create a proactive digital assistant that anticipates user needs, learns from interactions, and leverages dynamic tool use to provide personalized assistance in real-time. This system should be capable of understanding complex user contexts, synthesizing information from various sources, and initiating relevant actions without explicit prompting, mimicking a truly intelligent personal assistant.

Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Inspired by Apple's shift towards a personalized, Gemini-powered Siri, this challenge tasks you with building a sophisticated multi-agent system using Microsoft's AutoGen framework. The goal is to create a proactive digital assistant that anticipates user needs, learns from interactions, and leverages dynamic tool use to provide personalized assistance in real-time. This system should be capable of understanding complex user contexts, synthesizing information from various sources, and initiating relevant actions without explicit prompting, mimicking a truly intelligent personal assistant.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

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.

Max Score: 5
Dimensions
5 scoring checks
Binary
5 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1proactive_relevance

Proactive Relevance

Suggestion is contextually relevant and not a generic chatbot response.

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2tool_execution_success

Tool Execution Success

All necessary tools are identified and simulated execution is successful.

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 3multi_agent_coordination

Multi-Agent Coordination

Dialogue trace demonstrates clear, sequential, and logical agent interactions.

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 4personalization_score

Personalization Score

How well the agent's response reflects learned user preferences and history. • target: 0.8 • range: 0-1

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 5response_latency

Response Latency

Average time taken to generate a full response (simulated). • target: 3000 • range: 0-10000

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

Master AutoGen's conversational programming paradigm for defining complex multi-agent workflows and communication protocols.

Implement advanced prompt engineering techniques for Gemini 2.5 Pro to enable proactive, context-aware reasoning and response generation.

Integrate Deepgram's real-time Speech-to-Text and Text-to-Speech APIs for seamless voice interaction within the agent system.

Design and manage long-term personalized memory using Qdrant vector database for storing and retrieving user preferences, history, and context.

Build a dynamic tool invocation mechanism within AutoGen agents, allowing them to autonomously select and execute relevant actions or retrieve information via external APIs served by AI21 Studio's inference endpoints.

Orchestrate agent roles, such as a 'Context Analyst,' 'Action Planner,' and 'Information Retriever,' to collaborate effectively on complex user requests and proactive suggestions.

Deploy and manage multiple AI models using AI21 Studio's platform for efficient serving and routing of specialized tasks (e.g., summarization, entity extraction).

Start from your terminal
$npx -y @versalist/cli start build-proactive-personalized-assistant-with-autogen-gemini-2-5-pro

[ok] Wrote CHALLENGE.md

[ok] Wrote .versalist.json

[ok] Wrote eval/examples.json

Requires VERSALIST_API_KEY. Works with any MCP-aware editor.

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Challenge at a glance
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Vera

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Timeline and host

Operating window

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Tool Space Recipe

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Evaluation
Rubric: 5 dimensions
·Proactive Relevance(1%)
·Tool Execution Success(1%)
·Multi-Agent Coordination(1%)
·Personalization Score(1%)
·Response Latency(1%)
Gold items: 2 (2 public)

Frequently Asked Questions about Build Proactive Personalized Assistant with AutoGen & Gemini 2.5 Pro