“We automated 150 tasks with AI Agents, just copy us” - Microsoft AI
AI Summary
Podcast Summary
- Topic: Automating tasks with AI agents, impact of reasoning models, and personal use of AI agents at work.
- Guests: Two AI agent experts from Microsoft.
Windows Agent Arena
- Description: A benchmark for testing AI agents on real-world tasks in computing environments.
- Purpose: To measure agents’ performance on common tasks within Windows OS.
- Significance: Moves beyond memorization benchmarks to practical tasks that matter to users.
- Future of Agents: The experts anticipate agents that can handle less common applications with fewer users.
Agent Performance and Benchmarks
- Performance Metrics: New benchmarks like Mind Web, Web Arena, Visual Web Arena, UFO, OS World, and Android World are emerging.
- Human Performance: Casual Windows users have a 74% success rate on the benchmark tasks.
- Agent Adoption: For mainstream adoption, agents need high success rates and safe, robust ways for human intervention.
AI Agent Development
- Agent Components: Agents need to reason, plan, and execute.
- Microsoft’s Approach: They use an internal proprietary perception model and a planner agent.
- Customization vs. Accessibility: There’s a trade-off between ease of use and the ability to fine-tune details.
Future of Work with AI Agents
- Skill Level Impact: AI agents may assist beginners more than experts.
- Personal Preferences: Users will find unique ways to incorporate AI agents into their workflows.
- Security and Privacy: Certain industries may have restrictions on AI agent use.
AI Agent Interaction
- Modalities: Agents will likely use multiple modalities (text, voice, vision) depending on the task and user preferences.
- Custom Agents: The experts hope for a future where users can develop personalized agents.
AI Agent Platforms and Open Source
- Windows Agent Arena: A platform for testing and improving AI agents.
- Open Source vs. Closed Source: The competition between the two drives innovation.
Personal Use of AI Agents
- GitHub Copilot: Used by the experts for coding assistance.
- GPT Models: Used for reasoning and ideation.
Impact of Advanced Reasoning Models
- Reasoning Models: They offer the potential for handling complex tasks but may require more resources.
- Accessibility: There may be a divide in who can afford to run cutting-edge models.
Interacting with AI Agents
- Modalities: The ideal medium for interaction will depend on the task and user needs.
- Contextual Understanding: AI agents need to understand the context and inherent knowledge that humans possess.
Windows Agent Arena Usage
- Setup: Users can set up the environment and run benchmarks through GitHub.
- Contribution: Open for contributions to extend tasks and bring in new agents.
Detailed Instructions and URLs
- No specific CLI commands, website URLs, or detailed instructions were provided in the transcript.