DL – LLM, AI, DL, NLP.

Types of AI Agents

root — Wed, 12 Nov 2025 18:05:47 +0000

AI agents don’t all think and act in the same way. They range from simple rule-followers to systems that learn and adapt. Each type marks a step forward in how machines perceive, decide, and act.

Simple Reflex Agents: These follow condition–action rules. For example, if the temperature is high, turn on the fan. No memory, no thinking, just instant reaction. They are fast and simple.
Model-based Reflex Agents: These maintain an internal understanding of their environment. They are not just reacting to immediate inputs, they have a model that helps them make sense of what is happening beyond what they can see right now.
Goal-based Agents: Here, the focus shifts to goals. Decisions are made based on whether an action brings the agent closer to its objective.
Utility-based Agents: These go a step further by weighing different outcomes. They choose the action that offers the best overall result, balancing trade-offs along the way.
Learning Agents: These are the most advanced. They improve continuously, using feedback to adapt and perform better over time.

What are AI Agents?

root — Wed, 12 Nov 2025 18:04:50 +0000

Traditional software follows a predetermined path. However, AI agents can navigate uncertain situations and figure out what needs to be done.
AI Agents can perceive, decide, and adapt to achieve goals. This represents a significant leap from static programs to dynamic collaborations.
At its core, an AI agent works in a continuous cycle during which it perceives the current situation, thinks about what to do next, acts by taking a specific step, observes the results of the action, and then repeats the process. This cycle continues until the agent determines it has completed the task or needs human input to proceed further.

Multiple types of AI Agents exist, each supporting different capabilities:

1 – Simple Reflect Agents react to patterns, like thermostats or basic chatbots.
2 – Model-Based Agents build internal maps of their environment, enabling context-aware behavior.
3 – Goal-Based Agents can plan ahead and choose actions that serve specific objectives.
4 – Utility-Based Agents weigh trade-offs to find the best possible outcome.
5 – Learning Agents improve continuously by learning from feedback and experience.

AI agents are ushering in an era where software systems can become active collaborators.

CO2 Emissions application implementation (Jul-Aug 2024).

root — Mon, 19 Aug 2024 07:35:17 +0000

CO2 emissions application provides a comprehensive overview of carbon dioxide emissions by continent, focusing on emissions from coal, oil, and gas. Spanning from 1750 to the present, the dashboard offers detailed insights into how CO2 emissions have evolved over time, emphasizing the primary role of carbon dioxide in climate change. As the world faces the urgent need to reduce emissions, this tool highlights the issues of responsibility among and amount of emissions splitting for coal, oil, gas.