𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐂𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 by Data Science Dojo

I have attended the 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐂𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 by Data Science Dojo on 𝐒𝐞𝐩𝐭𝐞𝐦𝐛𝐞𝐫 𝟏𝟓–𝟏𝟗, 𝟐𝟎𝟐𝟓. This virtual conference brings together top industry experts to explore the building blocks of agentic AI – from memory, cognition, and planning to multi‑agent coordination, secure MCP deployment, and hands‑on tutorials with cutting‑edge frameworks. Here is a concise summary of the three Agentic AI Conference sessions I attended. 

💡 Architecting Scalable Multi-Agent Workflows 

This session explored strategies to build and operate multi-agent AI systems at scale, progressing from simple coordination to advanced orchestration. Industry experts shared methods to ensure effective communication, division of tasks, and state consistency across autonomous agents. Best practices were discussed for designing agentic architectures that maintain reliability, enable inter-agent collaboration, and support real-world, production-level deployments. 

💡 Managing Security and Governance in MCP Deployment 

Panelists addressed security and governance concerns when deploying Model Context Protocol (MCP) in AI ecosystems. They highlighted frameworks and operational controls needed to safeguard data, enforce compliance, and promote responsible, auditable use of agentic systems. Topics included access management, risk mitigation, and case studies illustrating best practices for keeping AI deployments safe and aligned with regulatory expectations.  

💡 Tutorial: Going Beyond the Chatbot with GitHub Webhooks 

This hands-on session demonstrated how to design event-driven AI agents that respond autonomously to real-world triggers by integrating with GitHub webhooks. Attendees learned to build agents capable of monitoring GitHub pull requests and executing actions (such as automated code review or closure) without human intervention. The tutorial covered connecting agentic workflows to external events, automating development processes, and implementing practical use cases for autonomous agents beyond conventional chatbots. These sessions collectively highlighted current best practices and innovative frameworks that push agentic AI beyond theoretical models and into scalable, secure, and industry-ready solutions.

AWS Lambda Cold Start Mitigations - User Experience Impact

 

 When I was building a Q&A platform backed by DynamoDB and FAISS, AWS Lambda seemed like the perfect engine. Event-driven. Scales infinitely. Pay-per-use. Perfect.

And then reality hit.

A simple user query would stall - not because DynamoDB was slow, not because FAISS was overloaded, but because Lambda was cold.

That extra 2–3 seconds feels trivial on paper, however…
❗ For payments, it frustrates customers.
❗ For security, it erodes trust.
❗ For a Q&A system, it shatters the illusion of instant knowledge.

💡AWS has answers :
Provisioned Concurrency - Keeps  a specified number of warm environments running at all times to eliminate cold starts.
SnapStart - Caches initialized microVMs, cutting startup from seconds to sub-second with minimal code changes.
Runtime Choice - Python and Node.js cold start faster than Java or .NET, making them better for latency-sensitive apps.

🟢 The real takeaway?
When designing with Lambda, let's not just evaluate features like scalability and cost. Let us also consider cold start mitigations and test how cold starts impact user experience.

References:
https://lnkd.in/eXKkRJwH
https://lnkd.in/em9pQPSN
https://lnkd.in/emuYq-kc

How Is AI Impacting The Environment?

Artificial intelligence (AI) is transforming industries, but it also raises important questions about sustainability. AI comes with significant environmental costs, yet it also offers tools to reduce humanity’s footprint. The impact depends on how we design, scale, and regulate these systems.

The Environmental Costs

Energy use
Training and running AI models is energy intensive. The International Energy Agency projects that global electricity demand from data centers could double between 2022 and 2026, partly driven by AI. By 2027, data centers could consume 4–6% of global electricity. In the U.S., they already account for over 4% of national usage [1].

Carbon emissions
Training OpenAI’s GPT-3 released an estimated 552 metric tons of CO₂—roughly the annual emissions of dozens of cars. Other studies show training cutting-edge models can equal hundreds of transatlantic flights or multiple times the lifetime emissions of an average vehicle [2][3].

Water consumption
Data centers require water for cooling. A study estimated training GPT-3 may have consumed 700,000 liters of freshwater. Looking ahead, AI could withdraw 4.2–6.6 billion cubic meters annually by 2027. Microsoft’s water use rose 34% from 2021 to 2022, and Google’s by 22%, linked partly to AI growth [4][5][6].

E-waste
AI chips and servers are resource-heavy to produce and replaced quickly. Manufacturing 2 kg of hardware can require up to 800 kg of raw materials, including rare earth metals. Analysts estimate AI could add 1.2–5 million metric tons of global e-waste by 2030 [7].

Grid strain
In hubs like Northern Virginia and Ireland, the sheer demand from data centers is pushing local power grids to their limits. In some regions, this pressure has even delayed the retirement of fossil fuel plants [8].

The Environmental Benefits

Smarter energy systems
AI helps grids balance supply and demand, enabling more reliable integration of intermittent renewables like solar and wind. This makes energy systems more efficient and resilient [9].

Better climate forecasts
AI is used to predict storms, droughts, and wildfires with greater accuracy by analyzing vast climate datasets. This improves preparedness and reduces human and economic losses [10].

Sustainable farming
Precision agriculture powered by AI can cut water and fertilizer use by up to 30% while boosting crop yields. Tools that combine satellite imagery and soil sensors reduce waste and environmental damage [11].

Efficient operations
AI optimizes shipping routes, supply chains, and factory processes. This cuts fuel use, emissions, and material waste, while improving efficiency across industries [12].

The Balance

AI’s environmental story is two-sided.

Costs include higher energy demand, more emissions, water stress, and e-waste.
Benefits include smarter use of resources, cleaner energy adoption, and better environmental monitoring.

The net impact depends on choices we make today: shifting data centers to renewable power, building energy-efficient AI models, and applying AI to sustainability challenges rather than just convenience.

Final Thought

AI is already shaping the environment—sometimes as a burden, sometimes as a solution. The challenge is to minimize its footprint while using its intelligence to help us build a more sustainable future.

References

1. International Energy Agency (IEA). (2023). Electricity 2023 – Analysis. https://www.iea.org/reports/electricity-2023-analysis
2. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. https://arxiv.org/abs/1906.02243
3. Patterson, D. et al. (2021). Carbon Emissions and Large Neural Network Training. https://arxiv.org/abs/2104.10350
4. UC Riverside. (2023). Making AI Less Thirsty: Uncovering and Addressing the Secret Water Footprint of AI Models. https://arxiv.org/abs/2304.03271
5. Microsoft. (2022). Microsoft Sustainability Report. https://www.microsoft.com/en-us/sustainability
6. Google. (2022). Google Environmental Report. https://sustainability.google/reports/
7. UNEP. (2023). Global E-Waste Monitor. https://www.unep.org/resources/global-ewaste-monitor-2023
8. Washington Post. (2025). AI’s ballooning energy consumption puts spotlight on data center efficiency. https://www.washingtonpost.com/ripple/2025/09/03/ais-ballooning-energy-consumption-puts-spotlight-on-data-center-efficiency/
9. Yale Climate Connections. (2025). What you need to know about AI and climate change. https://yaleclimateconnections.org/2025/09/what-you-need-to-know-about-ai-and-climate-change/
10. World Meteorological Organization. (2023). AI for Climate and Weather Prediction. https://public.wmo.int/en/resources
11. Food and Agriculture Organization (FAO). (2023). Digital Agriculture and AI. https://www.fao.org/digital-agriculture
12. MIT Sloan Management Review. (2024). AI Has High Data Center Energy Costs – But There Are Solutions. https://mitsloan.mit.edu/ideas-made-to-matter/ai-has-high-data-center-energy-costs-there-are-solutions