AWS wellarchitected documentation change
Summary
Added new subsection 'Optimize AI workloads for financial services requirements' covering latency, throughput, elasticity and multi-model orchestration. Expanded 'Use managed services' section with agent optimization techniques. Minor formatting changes to headers.
Security assessment
Changes focus exclusively on performance optimization for AI workloads (latency, throughput) and agent efficiency techniques. No security-related terminology, vulnerabilities, or security features are mentioned.
Diff
diff --git a/wellarchitected/latest/financial-services-industry-lens/performance-efficiency.md b/wellarchitected/latest/financial-services-industry-lens/performance-efficiency.md index a83ded176..2f35f2d85 100644 --- a//wellarchitected/latest/financial-services-industry-lens/performance-efficiency.md +++ b//wellarchitected/latest/financial-services-industry-lens/performance-efficiency.md @@ -19 +19 @@ In addition to the design principles in the AWS Well-Architected Framework white -**Consider both internal and external requirements** +### Consider both internal and external requirements @@ -23 +23 @@ Regulators expect financial services institutions to define operational performa -**Architect for performance-driven workloads** +### Architect for performance-driven workloads @@ -25 +25 @@ Regulators expect financial services institutions to define operational performa -Some financial services workloads, for example high-frequency trading systems and risk calculation engines, are particularly performance sensitive, with factors such as speed of completion and latency of response directly impacting the profitability of the system. Systems with considerations like these need to prioritize performance over other factors such as cost-efficiency or reliability, considering the trade-offs required to achieve their performance goals while also preserving non-functional requirements such as transactional consistency and recoverability. See the [Trade-offs](./perf-trade-offs.html) section of this pillar for more detail. +Some financial services workloads, for example high-frequency trading systems and risk calculation engines, are particularly performance sensitive, with factors such as speed of completion and latency of response directly impacting the profitability of the system. Systems with considerations like these need to prioritize performance over other factors such as cost-efficiency or reliability, considering the trade-offs required to achieve their performance goals while also preserving non- functional requirements such as transactional consistency and recoverability. For more detail, see [Trade-offs](./perf-trade-offs.html). @@ -27 +27,26 @@ Some financial services workloads, for example high-frequency trading systems an -**Use managed services** Leverage AWS cloud services to allow teams to use a wide range of technologies, to experiment with options and achieve their performance goals, while maintaining overall control. You can reduce the time it takes to configure, and invest in operations and on-going management, reducing operational overhead and using the right tool for the job. +#### Optimize AI workloads for financial services requirements + +Financial services AI workloads require specialized performance considerations due to regulatory requirements, real-time decision-making needs, and the sensitive nature of financial data. Design AI systems with performance goals in mind, considering factors such as: + + * Model inference latency for real-time fraud detection and trading systems + + * Throughput requirements for batch processing of regulatory reports and risk calculations + + * Resource elasticity to handle varying AI workload demands while maintaining cost efficiency + + * Multi-model orchestration to balance accuracy and performance across different financial use cases + + + + +**Use managed services:** Use AWS Cloud services to allow teams to use a wide range of technologies, to experiment with options and achieve their performance goals, while maintaining overall control. You can reduce the time it takes to configure, and invest in operations and on- going management, reducing operational overhead and using the right tool for the job. + +**Agent orchestration optimization:** Design efficient workflows for multi-agent systems. + +**Agent memory management:** Implement efficient context management for long-running agents. + +**Agent tool selection efficiency:** Optimize how agents select and use tools. + +**Agent parallelization:** Design patterns for parallel agent operations when appropriate. + +**Agent response time optimization:** Balance thoroughness with response time in agent operations. Selection