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Types of AI Agents
Govardhana M K
February 12, 2025
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TechOps Examples
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Along with a use case deep dive, we identify the remote job opportunities, top news, tools, and articles in the TechOps industry.
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IN TODAY'S EDITION
🧠 Use Case
Types of AI Agents
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🧠 USE CASE
Types of AI Agents
Welcome back to this week’s AI Series.
For the 400+ new subscribers since yesterday’s edition – typically, we discuss DevOps, Kubernetes, and Cloud use cases on weekdays.
We have also added at least one edition in the AI space over the past few weeks to keep up with this AI evolution.
In this AI series, we have covered so far:
Excited to share some new AI Free courses I found super useful.
Let’s move on to the topic of the day ‘Types of AI agents’.
AI agents can be built with different levels of intelligence depending on their purpose.
Below are five main types of AI agents:
1. Simple Reflex Agents
These agents operate purely based on the current input they receive, without memory or learning capabilities.
They follow predefined rules that dictate specific actions for specific conditions.
Example: An agent spins up new virtual machines when CPU usage exceeds 80% and shuts them down when it drops below 30%.
It does not consider trends or past usage - it simply reacts to the present state.
2. Model Based Reflex Agents
Unlike simple reflex agents, these maintain an internal representation of the system, updating it as new data arrives.
They can handle environments where all information is not immediately available.
Example: An agent that dynamically routes traffic based on real time server health checks and past failure patterns.
If one server frequently fails under heavy load, the agent updates its model and adjusts future traffic distribution accordingly.
3. Goal Based Agents
These agents go beyond reacting to inputs - they actively search for the best action sequence to reach a predefined goal.
They make decisions based on desired outcomes rather than just immediate conditions.
Example: An agent that evaluates multiple deployment strategies (e.g., blue green, canary, rolling) and selects the safest one based on service health metrics and error rates. If an issue is detected, it automatically rolls back.
4. Utility Based Agents
These agents don’t just aim to reach a goal - they also optimize for efficiency, cost, or other criteria using a utility function.
They evaluate different paths and choose the one that maximizes overall benefit.
Example: A cloud cost optimization agent that decides whether to run workloads on on demand instances, reserved instances, or spot instances.
It balances cost savings, performance needs, and availability risks to select the best option.
5. Learning Agents
These agents continuously improve by learning from past experiences. They refine their decision making over time and adapt to changing conditions.
Example: An AI powered incident management system that analyzes previous infrastructure failures, identifies recurring patterns, and proactively suggests fixes.
Over time, it improves its predictions and reduces downtime.
In the DevOps and Cloud context, some agent types are redundant since native capabilities already cover them, but they’re still worth learning about.
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