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An infographic comparing traditional software to AI agents, showing a technology stack with Large Language Models and a graph illustrating plummeting AI costs.

SLIDE 2: WHAT ARE AI AGENTS? ### Definition **Autonomous software systems powered by Large Language Models that execute complex EPC tasks with minimal human oversight** ### Technology Comparison | **Traditional Software** | → | **AI Agents** | |-------------------------|---|---------------| | Rule-based logic | → | Reasoning & judgment | | Explicit programming | → | Learning from examples | | Structured data only | → | Natural language, drawings, documents | | Single-task | → | Multi-task adaptable | | Static capability | → | Continuously improving | ### Technology Foundation - **Models:** GPT-4, Claude 3, Gemini 1.5 - **Accuracy:** 90-95% on engineering tasks - **Cost:** $0.15-$15 per 1M tokens (↓90% since 2020) - **Context:** Process entire projects (2M tokens = 50,000 pages) - **Integration:** API-connected to SAP, P6, Aveva, Bentley ### Why Now? ✓ **Technology matured** (2023-2024 breakthrough) ✓ **Proven ROI** (20-40% productivity gains industry-wide) ✓ **Competitive pressure** (68% of major firms piloting) ✓ **Talent shortage** (600K engineering positions unfilled by 2025) --- **VISUAL ELEMENTS:** - Side-by-side comparison infographic (Traditional vs. AI) - Technology stack diagram showing LLM integration - Cost reduction trend line (2020-2024) **REFERENCES:** OpenAI (openai.com/research), Anthropic (anthropic.com), Google AI (ai.google.dev), Gartner EPC Technology Report 2024 Mehr sehen

#technology #infographic #artificial intelligence

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