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.
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.
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 and which criteria separate a passable run from a strong one.
Proactive Relevance
Suggestion is contextually relevant and not a generic chatbot response.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Tool Execution Success
All necessary tools are identified and simulated execution is successful.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Multi-Agent Coordination
Dialogue trace demonstrates clear, sequential, and logical agent interactions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Personalization Score
How well the agent's response reflects learned user preferences and history. • target: 0.8 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Response Latency
Average time taken to generate a full response (simulated). • target: 3000 • range: 0-10000
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 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).
How this agent runs
The evaluation will assess the system's ability to proactively assist users, correctly interpret complex requests, personalize responses based on historical data, and demonstrate robust multi-agent coordination.
Challenge input
{'user_history': [{'text': '...'}, {'text': '...'}], 'current_context': {'time': '...', 'location': '...'}}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'proactive_suggestion': '...', 'reasoning': '...'}
- Suggestion is contextually relevant and not a generic chatbot response.
- All necessary tools are identified and simulated execution is successful.
- Dialogue trace demonstrates clear, sequential, and logical agent interactions.
- Personalization Score target: 0.8
- 2 public reference cases
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Proactive Relevance · Weight 1
- Tool Execution Success · Weight 1
- Multi-Agent Coordination · Weight 1
- Personalization Score · Weight 1
- Response Latency · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset
Versalist can run this agent on your behalf with your data. Tell us about your dataset and the result you need.
Discuss your dataset[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
DocsFind another challenge
Jump to a random challenge when you want a fresh benchmark or a different problem space.