tailored processes and strategies. The blue boxes presented throughout the guide pose a series of key questions to help acquisition professionals think critically about the Agile processes for a program an Agile Approach Agile represents a radical shift from industrial age processes to a modern management and development approach suited to the digital age. Agile practices help to make progress and information. However, Agile is not a panacea: it does not promise to solve all IT and program management problems, and may not be appropriate for use in all cases. Even successful adoption of Agile practices

Inference Costs Per Token Falling = Performance Converging + Developer Usage Rising 3 Cost of Key Technologies Relative to Launch Year % of Original Price By Year (Indexed to Year 0) Note: Per-token unprecedented rates is an understatement. Rapid and transformative technology innovation / adoption represent key underpinnings of these changes. As does leadership evolution for the global powers. Google’s founding relevant, with significant use). Source: Epoch AI (5/25) Training Dataset Size (Number of Words) for Key AI Models – 1950-2025, per Epoch AI Training Dataset Size – Number of Words +260% / Year AI Technology

integration, and security testing - this is a key DevSecOps differentiator since security and functional capabilities are tested and built simultaneously. Key Measures Mean-time to production: the average Mean-time to recovery: how long it takes applications in the production stage to recover from failure. Key Principles to implementing a successful DevSecOps approach  Remove bottlenecks (including human ones) Security Stack enables: correlated and centralized logs, container security, east/west traffic management, a zero-trust model, a whitelist, Role-Based Access Control (RBAC), continuous monitoring, signature-based

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 17 Modules 218 17.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 17.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Arrays inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency, package

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 17 Modules 218 17.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 17.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Arrays inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency, package

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 17 Modules 218 17.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 17.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Arrays inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency, package

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 17 Modules 218 17.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 17.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 31.6 Working with Arrays inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 35.5 Execution latency, package

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 17 Modules 219 17.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 17.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Arrays inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency, package

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 16 Modules 205 16.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 16.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 30.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 30.6 Working with Arrays The different prompt modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1400 85.2 Key bindings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1404 85.3 Tab

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 16 Modules 205 16.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 16.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 30.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 30.6 Working with Arrays The different prompt modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1400 85.2 Key bindings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1404 85.3 Tab