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apidays Munich 2025 - Agentic AI: A Friend or F...

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July 09, 2025
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apidays Munich 2025 - Agentic AI: A Friend or Foe?, Merja Kajava (Aavista Oy)

Agentic AI: A Friend or Foe?
Merja Kajava, Founder at Aavista Oy

apidays Munich 2025 - Accelerate AI Use Cases with APIs
July 2 & 3, 2025

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July 09, 2025
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  1. “What is the carbon footprint of a ChatGPT query compared

    to a Google query?” Photo credit: NASA / Alex Gerst
  2. Vanderbauwhede (2024): “AI-generated answers to conventional search queries dramatically increase

    the energy consumption. By our estimates, energy demand increase by 60-70 times.” Vanderbauwhede, W. (2024). Estimating the Increase in Emissions caused by AI-augmented Search. https://doi.org/10.48550/ARXIV.2407.16894
  3. Tools Orchestration Model Integrate to storage and 3rd party services

    Reason with goals Goals, profiles and guidance What are the elements of Agentic AI
  4. Agent-to-Agent (A2A) by Google Model Context Protocol (MCP) by Anthropic

    “UDDI for Agents” for agent discovery “BPEL for Agents” for orchestration NLWeb by Microsoft Agent Agent Agent <MCP> Tool <MCP> Tool <MCP> Tool RSS Schema.org Protocols are emerging for agent connectivity MCP MCP MCP A2A A2A MCP API MCP Servers <NLWeb> MCP Servers Agent Communication Protocol (ACP) by IBM Agent Connect Protocol (ACP) By Agntcy Collective
  5. Calculate the carbon footprint Cloud providers Data centers Service providers

    IPaaS API SaaS AI AWS Google Cloud Microsoft Multi-cloud Applications Carbon footprint calculators by cloud providers. Multi-cloud calculators. Open-source calculators, for example Code Carbon. Commercial calculators, for example from Dynatrace and Splunk.
  6. “Processing a single query might involve over 50 LLM API

    calls. The calls tend to be very narrow and specific. Different kinds of calls may be to different models.” NLWeb – Chat query example https://github.com/microsoft/NLWeb/blob/main/docs/life-of-a-chat-query.md Case NLWeb – Chat query example Sustainability Performance Cost
  7. AI Energy Score - Extractive Q & A by Huggingface

    Each Gen AI model is different Luccione et al. (2024): “Multi-purpose models are more energy-intensive.” Luccioni, S., Jernite, Y., & Strubell, E. (2024). Power Hungry Processing: Watts Driving the Cost of AI Deployment? The 2024 ACM Conference on Fairness, Accountability, and Transparency, 85–99. https://doi.org/10.1145/3630106.3658542
  8. Multimodal AI From text prompt to images,videos and speech Luccioni,

    S., Jernite, Y., & Strubell, E. (2024). Power Hungry Processing: Watts Driving the Cost of AI Deployment? The 2024 ACM Conference on Fairness, Accountability, and Transparency, 85–99. https://doi.org/10.1145/3630106.3658542 “Tasks involving images are more energy- and carbon-intensive compared to those involving text alone.”
  9. Key takeaways Choose the minimal solution - YAGNI Follow up

    the costs Measure the carbon footprint