A PM's Guide to AI Agent Architecture: Why Capability Doesn't Equal Adoption (11 minute read)

Building on the fundamental concepts, advanced a pm's guide to ai agent architecture: why capability doesn't equal adoption (11 minute read) (https://www.productcurious.com/p/a-pms-guide-to-ai-agent-architecture?) implementations require sophisticated techniques for optimal performance.

Cross-Modal Attention#

One of the most powerful techniques in a pm's guide to ai agent architecture: why capability doesn't equal adoption (11 minute read) (https://www.productcurious.com/p/a-pms-guide-to-ai-agent-architecture?) is cross-modal attention, which allows different modalities to attend to relevant information in other modalities. This creates a more holistic understanding of the input data by enabling the model to focus on the most relevant features across all available modalities.

Fusion Strategies#

Several fusion strategies can be employed:

• Early Fusion: Combining modalities at the input level for unified representation
• Late Fusion: Processing modalities separately then combining results at the decision level
• Hybrid Fusion: Using multiple fusion points throughout the processing pipeline

Optimization Approaches#

Optimizing a pm's guide to ai agent architecture: why capability doesn't equal adoption (11 minute read) (https://www.productcurious.com/p/a-pms-guide-to-ai-agent-architecture?) systems requires careful consideration of:

• Computational Efficiency: Balancing performance with resource constraints
• Training Strategies: Effective methods for training multi-modal models
• Evaluation Metrics: Comprehensive assessment of system performance