How do you define the requirements and scope of an intelligent agent compared to a traditional application?
How do you define the strategic goals of adopting an Agent Development Lifecycle (ADLC) versus traditional SDLC?
How should an organization decide which processes are suitable for agent-based automation versus traditional development?
ADLC > SDLC What are key points to consider for this shift? What are some applications that you used day to day to tackle the agentic world?
What shifts when moving from traditional Software Development Lifecycle (SDLC) to an Agent Development Lifecycle (ADLC)?
Is ADLC a replacement for SDLC, or a parallel discipline that overlays it?
How should an organization decide which processes are suitable for agent-based automation versus traditional development?
From SDLC to ADLC: The enterprise Agent Development Lifecycle
What’s the biggest mindset shift when moving from traditional SDLC to agent development?
What are the long-term architectural implications of adopting ADLC across multiple business units?
How different is ADLC from SDLC—do we need a brand-new playbook or just tweaks?
How do you architect for fault-tolerance and graceful degradation when agents fail in production?
What metrics and KPIs are critical to evaluate agent performance across their lifecycle?
How does ADLC account for an agent’s continuous learning and adaptation, unlike static software in SDLC?
How do you ensure AI agents are reliable at scale?
How do you approach testing for adaptive, non-deterministic agents?
How can organizations ensure that collaboration doesn’t slow down innovation but strengthens outcomes?
How should agents interact with legacy systems while maintaining performance and security?
How do you architect for fault-tolerance and graceful degradation when agents fail in production?
How do you measure ROI when transitioning from SDLC to ADLC in large-scale enterprises?