AI Solution Lifecycle Management.

An AI solution's lifecycle doesn't end at deployment the way traditional SDLC often treats "done." Because model behavior, usage patterns, and the underlying model version can all drift after launch, an architect owns a continuous loop of discovery, design, handoff, monitoring, and iteration, rather than a linear waterfall that stops at go-live.

Anthropic does not publish this exact five-phase list, under this exact name, in a single canonical document. It publishes several overlapping lifecycle frameworks across its own guidance, each emphasizing different stages of the same underlying shape: a five-stage loop (Planning → Prompting → Deployment → Evaluation → Optimization, looping back to Planning) in "Planning to production," and a four-stage loop (Develop AI strategy → Create business value → Build for production → Deploy, then "iterate, improve & expand" back to Stage 1) in "Building trusted AI in the enterprise." The exam's discovery/design/handoff/monitoring/iteration wording is a synthesis of this shared, real, if plural, continuous-loop shape - treat the specific five-phase labels as exam framing grounded in genuine Anthropic guidance, not a single direct quote.

Anthropic frames adoption itself as iterative, not a one-shot deployment. "Successful implementation of AI is iterative. Enterprises that are leading the way in AI transformation start small, evaluate thoroughly, and scale gradually before achieving their best results." This is the philosophy underlying every lifecycle phase below, not just the early ones.

"Planning to production" names its own five-stage loop explicitly as a cycle, not a line. Planning → Prompting → Deployment → Evaluation → Optimization, with Optimization's results feeding back into Planning. Mapped to the exam's phases: Planning ≈ discovery + design; Prompting ≈ design continuing into build; Deployment ≈ handoff; Evaluation ≈ monitoring; Optimization ≈ iteration. The loop-back arrow in Anthropic's own diagram IS the concrete version of "this is continuous, not linear."

Anthropic's own Deploy-stage guidance names the exact mistakes to avoid at the monitoring/iteration boundary. "Building trusted AI in the enterprise" lists explicit Don'ts for the Deploy stage: don't replace the previous system right away, don't treat your offline evaluations as static, and don't make a rollout decision based on a single evaluation test. This is the sourced, concrete version of "monitoring and iteration are load-bearing lifecycle phases, not optional post-launch cleanup."

AWS's cross-vendor GLOE framework maps cleanly onto the exam's phases, since lifecycle discipline for LLM systems isn't Anthropic-specific. The Generative AI Lifecycle Operational Excellence framework names three stages: Development (PoC and experimentation - discovery/design), Preproduction (validation and staging with beta users, plus setting up tracing/logging - handoff prep), and Production (deployment and continuous operations - monitoring/iteration), with Production feeding learnings back into Development.

Once live, the operational layer that keeps the loop closed has a name: LLMOps. Anthropic's own guidance lists five best practices for the Deploy stage: robust monitoring and observability, systematic prompt management, security and compliance by design, scalable infrastructure and cost management, and continuous quality assurance (regular output testing, hallucination monitoring, end-user feedback loops). A model-version upgrade is treated as a change needing re-validation against the eval suite, never a free improvement.