IMPACTE: An AI-First Software Engineering FrameworkAcronym for: Intelligent Multi-Agent Product-Centric Architecture with Cost-Efficiency and Trade-offs Engineering IMPACTE explores the emerging paradigm shift in software engineering where the primary bottleneck may be moving from implementation velocity to problem definition accuracy and regulatory compliance. The framework investigates how the Software Development Lifecycle (SDLC) might be restructured around two core principles: 1. AI-First Execution: LLMs would serve as primary implementation agents rather than assistants2. Research-Driven Oversight: Human engineers would focus on architectural validation, compliance verification, and economic optimization rather than syntax generation Key Features Explored: Heterogeneous Multi-Model Orchestration: Specialized AI agents for Strategic Planning (Reasoning Models), Implementation (High-Context Coding Models), and Infrastructure Management (Long-Context Agents) Policy-as-Code Governance: A proposed zero-trust verification pipeline with cross-model validation and automated quality gates Document-as-Code (DaC): PRD/RFC generation before implementation, intended to support audit trail compliance Token Economics Management: Cost optimization and model routing strategies Test-Driven AI Development: Automated test generation with coverage thresholds This framework has been explored in early-stage healthcare and financial technology environments. Preliminary observations suggest a reallocation of engineering labor from low-level coding tasks (appearing to consume <20% of time) toward higher-value architectural and compliance activities. Contents: Reference implementation codebase Configuration templates for deterministic agent behavior Governance rule examples Pilot study data and observations Keywords: LLM Agents, AI-First Development, Software Engineering, Compliance, FinTech, HealthTech, Regulatory Technology, DevOps, Multi-Agent Systems, Token Economics