Learn the Core Components of AI Agents

The Brain of the Operation: Reasoning and LLMs

Ever wonder how a bot actually "decides" to help you instead of just spitting out random text? It's all about the brain, which in this case, is the llm.

Think of the llm as the core reasoning unit. It isn't just a database; it's the part that processes instructions and makes calls on what to do next. As explained in the Introduction to generative AI and agents training by Microsoft, these models are what allow an agent to actually understand a prompt and act on it.

• Reasoning: The model looks at a goal—like "fix this retail supply chain delay"—and figures out the logic. If the first step fails, the agent doesn't just quit; it re-evaluates the situation and tries a different logical path.
• Model Size: You don't always need the biggest, most expensive model. Smaller ones work great for simple tasks and save a ton of cash. (16 Side Jobs You Can Do While Also Working Full Time)
• Decision Making: It picks which tools to use based on the context you give it. For really complex stuff, you can even use a "multi-agent" setup where different brains handle different parts of the job—we'll get more into that advanced config later.

When a big goal hits, the agent breaks it down. This is called chain of thought. It’s like how a human doesn't just "build a house," they start with the foundation.

If things go sideways—say, the logic doesn't add up—the agent has to rethink the plan on the fly. It's not just following a script; it's trying to solve the problem. Next, we'll look at how these brains actually remember what they're doing.

Memory Systems: Keeping the Context

Ever tried talking to someone who forgets your name every five minutes? It's exhausting, and honestly, ai agents without memory are just as annoying.

To do anything useful, agents need to "remember" what happened two seconds ago and what happened last month. As explained in the AI Agents for Beginners series on microsoft learn, this is often handled through a pattern called agentic rag.

• Context Windows: This is the "short-term" stuff. It's the immediate chat history—like a doctor remembering the symptoms you just listed.
• Vector Databases: This is the "long-term" storage. It lets an agent search through massive amounts of old data—like a retail bot looking up a return policy from 2023.
• agentic rag (retrieval augmented generation): This isn't just a static search. In an "agentic" setup, the agent actively decides what to search for, evaluates if the results are actually helpful, and goes back for more if the data is junk. It's basically the agent doing its own research.

In sectors like finance, memory is huge for spotting patterns. If an agent remembers your usual spending, it can flag a weird $5,000 charge. (Franklin Police Department's post - Facebook) But we gotta be careful—storing all this "history" raises big privacy red flags. Always encrypt that data, okay?

Next, we're gonna talk about how these agents actually do things using tools.

Tools and Action: Moving Beyond Chat

So, you got a brain and a memory—great. But if your agent just sits there "thinking" without actually doing anything, it's basically just a expensive philosopher. To be useful, agents need to reach out and touch the real world.

This is where tool use (sometimes called function calling) comes in. It’s how an agent stops just talking and starts acting. If a customer asks a retail bot where their package is, the agent doesn't guess; it triggers an api call to a shipping provider. If that finance api returns an error, the agent sees it and tries a different tool. Technokeens actually helps businesses bridge these gaps by plugging agents into messy, old tech stacks so they can actually send emails or update crm records.

• Browsing: Agents can use search tools to find live data, like current stock prices.
• Action: They can "click" buttons in software to, say, book a flight or generate an invoice.
• Human-in-the-loop (HITL): For big stuff like payments or deleting files, you need a human to hit "approve." You don't want an ai accidentally spending your whole budget without a thumbs up.
• Safety: You gotta set strict permissions so an ai doesn't accidentally delete your whole database.

Sometimes one agent is just too overwhelmed. Like in a big marketing campaign—you might have one agent focused on seo, another on graphic prompts, and a "manager" agent keeping them in sync. This is that multi-agent approach I mentioned earlier; it's killer for complex workflows because it's like having a whole team instead of one overworked intern.

Honestly, watching two agents "talk" to solve a problem is pretty wild. It’s like a digital department that never sleeps. Next, we'll wrap things up by looking at how to keep these agents safe and under control.

Security and Governance for the Enterprise

Building an ai agent for the enterprise is cool and all until someone accidentally leaks the entire customer database because a prompt was "too persuasive." Honestly, without solid governance, you're just handing the keys of your company to a very fast, very unpredictable intern.

Security: Protecting the System

You can't just let agents run wild with a generic admin account. Each agent needs its own identity—think of it like a service account but for an ai. This is where zero trust comes in; never trust, always verify every single api call.

• Identity Management: Give agents specific roles using RBAC (Role-Based Access Control) so the marketing bot can't touch payroll.
• Prompt Injection: Use filters to stop users from "tricking" the agent into ignoring its safety rules.
• Data Privacy: Ensure PII (Personally Identifiable Information) like names or social security numbers is masked before it even hits the llm.

Governance and Operations: Monitoring the Mess

As mentioned earlier in the series on microsoft learn, you gotta track performance or costs will spiral. I've seen teams spend thousands on "looping" agents that didn't even finish the task.

• Audit Trails: Log every decision so you know why the agent bought 500 printers.
• Cost Tracking: Monitor token usage per agent to avoid budget heart attacks.
• Retirement: If an agent's model is outdated or it's making too many mistakes, kill the process and update it.

So, wrapping up this whole thing—agents are the future of digital transformation, but only if they're built with a brain, a memory, the right tools, and a very tight leash. Good luck out there!