ALIVE: An Agentic Longitudinal IDE-IPA Value Evaluation Framework for Innovation-Driven Enterprise Assessment - Chaoraingern, Jutarut; Numsomran, Arjin (My University, 2026)
Agentic artificial intelligence architectures have demonstrated transformative potential for automating complex, multi-dimensional evaluation tasks; however, existing deployments remain predominantly stateless, incapable of accumulating institutional knowledge across evaluation cycles and therefore structurally unfit for longitudinal domains such as Impact Pathway Assessment and Social Return on Investment scoring, which inherently demand temporal coherence and precedent-informed judgment. This article presents ALIVE: an Agentic Longitudinal IDE-IPA Value Evaluation Framework, a sixth-generation (L6) multi-agent architecture addressing this limitation through the PACT Loop: Perceive normalizes inputs and retrieves episodic priors from ChromaDB; Analyze scores 18 dimensions in parallel via a shared rubric loaded once across all agents; Converge applies an auto-calibrating halting rule terminating on score stability, target achievement, or budget exhaustion; and Transfer extracts lessons via idempotent atom derivation, matches peer patterns, and broadcasts learnings, with longitudinal state in PostgreSQL and ChromaDB ensuring each cycle improves the next. Four agents (Funder, Company, Researcher, and Society), each instantiated as a role-specific skill context, are orchestrated by Claude via 21 stateless MCP tools on Railway.app without server-side API key. A self-evaluation loop scores each response against a structured rubric, triggering retry until a quality-gate is satisfied. ALIVE operationalizes the IDE-IPA Analyzer-Pro V2.0, a 100-point, 18-dimension rubric spanning Standard IDE Assessment (Part A), Research-Specific Assessment (Part B), Impact Pathway Logic (Part C), and SROI Assessment (Part D). Validation on thirty synthetic proposals across six industry domains against a three-expert panel demonstrates ICC21=0.922 (≥ 0.80 good-agreement threshold), funding decision accuracy of 86.7%, and sub-five-minute processing per proposal, establishing ALIVE as a scalable, self-improving infrastructure for longitudinal impact pathway assessment in research funding administration.
Keywords: Agentic artificial intelligence; impact pathway assessment; longitudinal evaluation; multi-agent systems; workflow orchestration