The Agentic Development Lifecycle (ADLC) Framework
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the gist
ADLC replaces the static SDLC with a seven-phase framework designed for non-deterministic AI agents, focusing on continuous evaluation, behavioral planning, and human-in-the-loop accountability.
The Shift from SDLC to ADLC
Traditional Software Development Life Cycle (SDLC) assumes static, predictable code where inputs yield consistent outputs. AI agents are inherently non-deterministic, meaning the same prompt can produce different results based on context, model updates, and tool availability. The Agentic Development Lifecycle (ADLC) replaces static pass-fail testing with continuous evaluation and behavioral monitoring to manage this unpredictability.
The Seven Phases of ADLC
- Preparation and Hypothesis: Use an agent in 'planning mode' to map workflows and identify manual bottlenecks before writing code. Formulate testable hypotheses about where agents can automate tasks.
- Scope and Problem Identification: Define technical KPIs (latency, cost, accuracy) and establish a human-agent responsibility model. This phase ensures human accountability for agent decisions and sets clear autonomy boundaries.
- Design and Architecture: Select the agent pattern (e.g., ReAct, Plan-and-Act, or multi-agent) and define token economics, context management strategies, and data flow. Use agent planning prompts to document trade-offs before implementation.
- Simulation and Proof of Value: Build prototypes to validate high-risk assumptions against real-world data. Establish performance baselines and ground truth documents to serve as assets for future regression testing.
- Implementation: Develop core logic while managing the intersection of code, prompts, models, and external tools. Use orchestration layers (e.g., Claude Code agents view) and integrate tools via MCPs while actively managing context to prevent 'context rot'.
- Testing: Shift from functional pass-fail testing to continuous evaluation of reasoning, safety, and tool use. Utilize frameworks like Ragas or DeepEval to measure accuracy distribution and hallucination rates under production-like conditions.
- Deployment and Maintenance: Treat deployment as a controlled activation rather than a final release. Implement active monitoring for behavioral drift and use UI feedback signals (e.g., thumbs up/down) to drive continuous learning and model refinement.