Agent-Native-SDLC

Agent-Native SDLC (Software Factory Pattern)

Agent-Native SDLC is an architectural shift where the entire software development lifecycle—from requirements to deployment—is designed for collaboration between human stakeholders and autonomous AI agents.

🏭 The “Software Factory” Model

Unlike traditional “Single-Player” AI coding tools (which focus on the IDE), the Software Factory model focuses on the Orchestration Layer.

1. Upstream Decision Capture

The system captures the “Why” before the “How.”

• Requirements: Structured, agent-readable business intent.
• Architecture: Explicit trade-offs and component maps.
• Context Engineering: Bridging the gap between a PM’s document and an Engineer’s implementation.

2. The Knowledge Graph

At the core of an Agent-Native system is a Knowledge Graph that links every artifact:

• Requirement A → Architectural Decision B → Code Implementation C.
• Propagation: If Requirement A changes, the system identifies all impacted decisions and code, notifying agents to refactor or update context.

3. Specialized Agent Roles

Instead of one generalist agent, the system uses a “Symphony” of specialized agents:

• Architect Agent: Validates design constraints.
• Implementation Agent: Writes code based on structured plans.
• QA Agent: Generates tests against the original requirements.

🚀 Strategic Benefits

• Zero Tribal Knowledge: Decisions are documented in the graph, not just in Slack or heads.
• Coherence at Scale: Agents maintain a global view of the project that humans often lose as complexity grows.
• Synchronized Updates: Reduces “context drift” between documentation and reality.

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Source: Ingested from Introducing 8090