Specifying AI-SDLC Processes: A Protocol Language for Human-Agent Boundaries

• URL: http://arxiv.org/abs/2606.20615v2
• Date: Wed, 24 Jun 2026 00:52:02 GMT
• Title: Specifying AI-SDLC Processes: A Protocol Language for Human-Agent Boundaries
• Abstract summary: We propose a domain-specific language for specifying AI-SDLC processes as protocols.<n>The language distinguishes policy (declared intent) from mechanism (structural enforcement)<n>A working implementation demonstrates feasibility; empirical evaluation is future work.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI agents now participate as first-class team members across the software development lifecycle, yet no specification language exists for expressing the human-agent responsibility boundaries, approval gates, and governance constraints this collaboration requires. Existing approaches encode process in agent prompts (subject to drift), target adjacent domains (workflow management, business processes), or address only fragments (access control, approval gates). We propose a domain-specific language for specifying AI-SDLC processes as protocols, with formal syntax, well-formedness conditions, operational semantics, and enforcement invariants. The language distinguishes policy (declared intent) from mechanism (structural enforcement), enabling implementations to bound process non-determinism through primitives such as validation tokens and capability boundaries. Three results follow. A failure rate analysis shows that structural enforcement bounds system failure rates at a weighted product of agent and validator rates, while behavioral compliance permits cumulative or near-saturating growth. The 2+N team pattern (two human-in-control roles plus N specialized agent members) formalizes classical Separation of Duties for AI-SDLC. Kleene closure of orchestration loops and reflexive protocol-adherence validation emerge as design properties rather than special-case constructs. We position the contribution against multi-agent frameworks (MetaGPT), workflow specification (FlowAgent, BPMN extensions), and capability-based security (SAGA): the novelty lies in the specific integration, not any single primitive. A working implementation demonstrates feasibility; empirical evaluation is future work.

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