AI Agent types

Different “agent types” emerge depending on their role in the system:

1. Perception Agents
    
   • Use cameras (RGB, depth, motion capture) to detect body position, angles, velocity.
      
   • Models: pose estimation (e.g., MediaPipe, OpenPose, DeepLabCut).
      

1. Analysis Agents
    
   • Take raw perception data → calculate biomechanics (joint angles, timing, force approximations).
      
   • Compare performance to “ideal form” models (pro athlete libraries, coaching heuristics).
      

1. Decision/Feedback Agents
    
   • Translate analysis into plain language: “Your release point is too low; raise elbow by ~10°.”
      
   • Provide real-time corrective cues (visual overlay, AR coach, haptic feedback).
      

1. Knowledge Agents
    
   • Build and manage the knowledge base — library of thousands of “reference throws” tagged with fundamentals.
      
   • Train continuously from more samples.
      

1. Interface/Engagement Agents
    
   • Handle user experience: dashboards, mobile apps, AR glasses, or web portals.
      
   • Tailor feedback depending on audience (player, coach, scout, fan).
      

You’ll need a physical setup to capture reliable data:

• Cameras
   
  • High-frame-rate RGB cameras (120–240fps) for form breakdown.
     
  • Depth cameras (Intel RealSense, Azure Kinect, iPhone LiDAR) for 3D skeleton mapping.
     
  • Optional: motion capture suits or markers for high-precision biomechanics (expensive, ~$20k–$150k).
     
• Edge Computing (for real-time feedback)
   
  • GPU-enabled local compute (NVIDIA Jetson, small RTX servers).
     
  • Runs pose estimation + first-pass analysis before sending data to cloud.
     
• Networking
   
  • Reliable Wi-Fi or 5G if streaming data live.
     
  • Buffering system to prevent lag in real-time coaching.
     

Here’s the flow of how an AI Agent would process:

1. Capture Layer
    
   • Cameras capture 2D/3D skeletal frames.
      
   • Pre-processing filters noise.
      

1. Pose Estimation Layer
    
   • OpenPose/MediaPipe generates 3D skeleton (joint coordinates).
      
   • Time-series data of movement.
      

1. Biomechanics Analysis Layer
    
   • Calculate joint angles, angular velocity, stride length, hip-shoulder separation, etc.
      
   • Compare against thresholds for “good form.”
      

1. AI Feedback Layer
    
   • Machine learning trained on thousands of throws.
      
   • Generates specific coaching advice: “arm lag detected,” “foot plant early.”
      

1. UI/UX Layer
    
   • Cloud dashboards for reviewing sessions.
      
   • Real-time overlay on video (like telestration).
      
   • AR/VR extension for immersive coaching.
      

• Cloud Data Lake: Raw video + skeletal data stored (AWS S3, GCP Storage).
   
• Structured Database: Athlete profiles, performance logs, comparisons.
   
• Model Hosting: ML models deployed via APIs (AWS SageMaker, Vertex AI, Azure ML).
   
• Access Layer: Secure coach/athlete logins, dashboards, reports.
   

For live applications (game, practice):

• Stream camera input → edge device computes → sends compressed skeletal vectors (not full video) to cloud → cloud ML model returns instant feedback.
   
• Latency goal: <200ms for AR/VR “coaching overlays.”
   

Where you can spin AI Agents into business:

• Athlete Development Platforms: subscription for players, parents, coaches.
   
• Team Integration: integrate with pro/college training rooms.
   
• Sports Medicine: injury prevention (detecting stress-inducing mechanics).
   
• Fan Engagement: AR apps showing “throw breakdowns” for entertainment.
   

Summary:
Many of the required AI Agents of today require an AI biomechanics lab with the following capabilities:

• Capture hardware (cameras, optional mocap).
   
• Edge + cloud compute stack.
   
• Pose estimation + biomechanics AI.
   
• Real-time coaching agent that delivers feedback.
   
• UI/UX for athletes and coaches.
   

Constructing AI Agent types requires a trick in balancing accuracy vs cost vs usability. A pro-grade mocap lab gives precise data, but costs hundreds of thousands. Using AI + commodity visual devices gives 80% of the value at 20% of the cost — perfect for a scalable commercial product. Design and layout is key.