Using LLM Agents for Strategic Goals Extraction and Decision-Making
| dc.contributor.author | HADEG Mohamed Abdelmalek | |
| dc.contributor.author | DJAGHET Mohamed | |
| dc.contributor.author | RAHIM Messaoud (Supervised) | |
| dc.contributor.author | RAFA Tahar (Co-supervised) | |
| dc.date.accessioned | 2026-10-12T09:13:28Z | |
| dc.date.available | 2026-10-12T09:13:28Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | Strategic planning is vital for organizations, but drafting institutional strategies and aligned unit-level goals is complex. Large Language Models (LLMs) can support this through language understanding and generation, though direct use risks unsupported, misaligned, or overly automated decisions. This project presents StratAI, a web application developed as a supervised LLM-agentbased decision-support system for institutional strategy drafting and administrative-unitlevel strategic goal derivation. The system is designed around an architecture in which LLM-based capabilities are used inside controlled stages rather than as fully autonomous decision-makers | |
| dc.identifier.citation | Applied Artificial Intelligence | |
| dc.identifier.uri | https://dspace.univ-medea.dz/handle/123456789/14525 | |
| dc.language.iso | en | |
| dc.relation.ispartofseries | M62635; M62635 | |
| dc.title | Using LLM Agents for Strategic Goals Extraction and Decision-Making | |
| dc.type | Learning Object |
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