ADLC: Claude Code's New Lifecycle for AI Coding

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AI agents do not fit the old SDLC, and ai coding teams are paying the price. An ai coding assistant behaves differently than traditional software, so vibe coding and modern ai programming need a lifecycle built for non-determinism, context drift, and continuous evaluation.

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In this video, we walk you through the Agentic Development Lifecycle, the framework that replaces the old SDLC for teams doing ai coding with agentic systems. Traditional software was predictable, but coding with ai changed everything, because the same prompt can return different outputs, and you cannot grade an agent the way you grade static software.

We break down all seven phases of ADLC, from planning and analysis to design, validation, implementation, testing, deployment, and maintenance. Whether you are doing vibe coding ai prototypes in Claude Code or shipping production agents, this lifecycle gives you the structure most coding ai projects skip entirely.

You will learn why the best coding ai workflows always start in planning mode, how to map the human and agent responsibility model, and why ai for coding now means continuous evaluation instead of pass or fail testing. We also cover claude ai coding tradeoffs like context management, model selection, cost economics, and how to avoid context rot when working with large context windows.

This is an easy ai coding guide for anyone who has been jumping straight into building agents without a proper lifecycle. Once you adopt this framework, ai vibe coding stops feeling chaotic, and you start shipping ai agent systems that actually hold up in production. We also touch on the ai coding tools that fit into each phase, from the new Claude Code agents view for orchestration to MCP integrations for tool access, plus feedback signals seen in production tools like Claude and ChatGPT.

If you are serious about ai agents, claude code, or any modern vibe coding stack, this lifecycle is the difference between a prototype that breaks and a system that scales.

0:00 Intro
0:32 What SDLC is and why it broke
1:25 Why ADLC was needed
2:29 The 7 phases of ADLC
2:36 Phase 1: Preparation and hypothesis
4:10 Phase 2: Scope and problem identification
5:38 Phase 3: Design and architecture
6:51 Phase 4: Simulation and proof of value
8:02 Sponsor
8:58 Phase 5: Implementation
11:02 Phase 6: Testing
12:34 Phase 7: Deployment
13:53 Maintenance and continuous learning

Hashtags:
#claude #claudecode #ai #chatgpt #vibecoding #aiagent #aiagents #aicoding

Видео ADLC: Claude Code's New Lifecycle for AI Coding канала AI LABS