Tony Stark Had Jarvis—You Have Agentic AI
Queries are dragging, deadlocks keep piling up, cryptic error messages, and the logs? A total mess. Instead of working on the big, important stuff, you're stuck chasing down bugs or cleaning up simple performance issues. It's frustrating, and you've probably thought, "There's gotta be a better way."
Well, there is. And it's a big deal with lots of stoke. Agentic AI isn't just another AI buzzword; it's the next big deal in tech, and it's already starting to show up everywhere. From automating database tuning to optimizing workflows (ie, the work we do), these AI-powered helpers don't just analyze problems—they actually fix them. They can figure out what needs to be done, how to do it, and then get the job done. Possibly all on their own.
Though the term isn't neatly defined across the tech sector yet, AI agents are a step beyond chatbots in that they're typically designed for specific business tasks or a flow of work. But, like chatbots, they make heavy use of LLMs. Remember Tony Stark's Jarvis? Great example.
For me, it's already changing how I create tools, explore and do experiments, and even build apps. AI agents are changing nearly every area of my work and life.
In this post, I'll break down what Agentic AI is, a little about how it works, and why it's the tool you didn't know you needed. Less busywork, more time for high-value work. Let's jump in.
What is Agentic AI?
At its core, Agentic AI or AI Agents is about action. It's AI that doesn't just sit there analyzing things. It's AI that actually does something about what it finds.
Agentic AI-powered systems are designed to plan, decide, and (gulp) execute tasks on their own, all while working toward a specific goal. Think of them like digital assistants that don't just tell you what's broken. They can roll up their sleeves and start fixing it.
Think of it like this: Traditional AI has been great for analyzing data, generating insights, or answering questions, but it always needed you to take the next step. That's why they are called "assistants." Agentic AI, on the other hand, can figure out what to do and follow through, often without much supervision. That's why they are called "agents."
Let's say a database query is running slow. Instead of just flagging it, an Agentic AI can identify the issue, rewrite the query, test it, and even deploy the fix! And all while keeping you in the loop.
Sound futuristic? It's already happening. Even before the term "Agentic AI" was being used, the Oracle Autonomous Database and Oracle 23ai have been embracing agentic principles. This really is the vision of the Autonomous Database coming to life.
Real-World Use Cases for Developers and DBAs
So, what makes Agentic AI such a big deal for developers and DBAs? It's all about automating the stuff that eats up your day, so you're not a babysitter. (Don't you feel that way sometimes?)
Here's what you can expect in production, not just in PowerPoint:
- Query Optimization and Performance Tuning
Imagine an AI agent constantly monitoring your database. It spots slow queries, analyzes execution plans, suggests optimizations, and even applies fixes if you let it. No more spending hours digging through query stats. You get cleaner, faster performance with minimal effort. It's like having a performance-tuning expert on standby.
Oracle 23ai has taken this up a huge notch, and you can expect this to accelerate. - Troubleshooting, Diagnostics, and Log Analysis
For example, agents can detect deadlocks, identify resource-hogging processes, and even fix minor performance bottlenecks on their own. For developers, this means less time sifting through logs and staring at the screen. For DBAs, fewer late-night fire drills.
Oracle does a lot of this internally, but dealing with application-level deadlocks takes it to a whole new level. - Proactive Security Monitoring
Agentic AI keeps an eye on suspicious activity—unauthorized access attempts, abnormal queries, or other red flags. It can notify you or even take action to enforce policies and secure your databases.
While ML is already playing a part in this, AI and now Agentic AI have the ability to go much further because they can take action using other systems and agents. - Automated Code Assistance
"Code Assist" is already here today, and it is truly mind-blowing! Oracle is already demoing and beta-testing its "AI Code Companion" (search: Oracle Code Assist). I use Cursor AI (search: Cursor AI) nearly every day now.
My coding workflow works like this: I tell the AI what I want, it gives me options, I tell it what to do, and it does it. If there is a bug, I simply paste the output into the AI, and it quickly tells me what's wrong.
I don't think I will ever code again while looking at an empty editor. Seriously. Development will never, ever be the same. - Multi-Step Workflows & Problem Solving
Here's the kicker: Agentic AI doesn't just handle single tasks. It plans, executes, and validates multi-step processes, like identifying a slow query, testing a fix, deploying it, and monitoring the results. This is a huge opportunity, and the industry is just getting started.
Complex problems that require significant reasoning are really difficult for LLMs. Agents use LLMs to help them solve problems and determine what to do next. But the LLM does not actually do what's next. The agent does what's next. The LLM now plays a supporting role.
Bottom line? Agentic AI isn't just "smart"; it's active and proactive. It takes care of the repetitive, tedious stuff so you can focus on the high-value work.
Agentic AI Examples
I'm going to show you two quick Agentic AI examples.
Watch the AIs Debate The first example is two AIs engaged in a debate. Notice how the AIs respond to each other: they first respond and then challenge. They are also respectful because I prompted them to behave that way.
I would say this is a very basic agent because all the reference material is within the LLM. That is, the agent does not go out into the world to gather reference material or do any fact-checking.
Debate Simulator v0.0.2 9-Dec-2024
=== Debate Starting ===
Topic: Discuss the following topic: Are novel solutions a distinctly human trait that AI can never match? Present your perspective and respond to your opponent's arguments respectfully and factually. Do not repeat the same arguments.
Advocate: AI struggles with novelty, lacking human creativity. It's limited by data, and can't truly innovate like humans, who create from imagination.
Skeptic: AI demonstrates innovative problem-solving in games, art, and science, offering unique solutions beyond human creativity. This refutes the 'AI can't be novel' claim.
--- Turn 1 ---
Advocate: AI's 'novelty' is derivative, not truly original. Human creativity stems from emotion, intuition, and imagination, which AI lacks.
Skeptic: AI's novelty isn't derivative; it's a new synthesis of existing elements, akin to human creativity, but with a broader, unbiased scope.
--- Turn 2 ---
Advocate: Human creativity is unique, deeply personal, and inherently unpredictable. AI, by design, cannot replicate this complexity.
Skeptic: AI's creative process, though different, can yield unexpected, original outputs, challenging the notion of human exclusivity.
What I like about this example is that if you understand what is happening, you will naturally think of ways to give the AIs more power. And this is exactly what the industry is doing and building: the ability for agents to make decisions and then act, even outside of their own code base.
If you would like to download the short Python program, CLICK HERE.
Agentic Code Assistance
The AI Debate program I referenced above was created with just a single prompt. Then I directed the AI to make specific changes that I did not originally think about. I also made some changes myself. It was a collaborative effort.
If you look closely at the image below, you will notice some code on the left. But the interesting part is on the right. This is where I am dialoguing with the AI, and it shows me what it wants to do along with a textual description. If you click on the image, you will see a larger version with all the details.
So how does this all work? Seem complicated? The next section should help you understand a little more about what's going on.
Tools and Technologies Behind Agentic AI
So, what's powering all this Agentic AI magic? There is a quickly evolving mix of tools and technologies working together to make these AI systems proactive, capable, and downright (sometimes scary) useful.
- Large Language Models (LLMs)
At the heart of Agentic AI are models like LLaMa, Command-R, GPT-4, or Claude. These powerhouses give agents the ability to reason, plan, and adapt to changing conditions. - Orchestration Frameworks
The frameworks are the glue that ties it all together. They help AI agents string tasks into multi-step workflows, like analyzing a slow query, fixing it, testing it, and reporting back. Tools like LangChain, Microsoft AutoGen, CrewAI, Hugging Face Transformers Agents 2, and metaGPT are just a few of the frameworks vying for market share.
Let's be honest. These frameworks are essentially heuristics (if-then-else statements) that help us direct the AIs to do what we want them to do. - APIs and System Integrations
Agentic AI doesn't live in a vacuum. It connects to your databases, application logs, CI/CD pipelines, and monitoring tools through APIs. Whether it's SQL performance dashboards, cloud services, or security systems, agents pull in data and take action where it's needed.
The most useful agents will absolutely connect and work with the "outside" world. - Feedback Loops
Here's where things get really cool: Agents validate their actions and learn over time. If a fix works, great—they'll use that knowledge again. If not, they adjust.
Combine all these pieces, and you've got AI that acts, not just analyzes. Whether it's Oracle 23ai automating database optimizations, a simulated debate, or a custom LangChain agent handling your personal finances, the tools are already here. What we have now will soon seem so simple.
Challenges and Considerations
Agentic AI sounds great—and it is—but it's not all stoke and perfectly optimized queries. Like any new technology, there are a few things to keep in mind:
- Trust and Accuracy
AI agents are smart, but they're not perfect. Sometimes the "fix" they suggest isn't the best one, so human oversight is still important. You wouldn't let a rookie touch production without supervision, right? Same deal here. - Integration Complexity
Getting Agentic AI up and running in your environment can take effort. Connecting it to databases, CI/CD pipelines, or monitoring tools might mean some setup time. This is especially true if you're working with older systems. - Security Risks
Agents often need access to your data, logs, or workflows, which means security is a big deal. Proper permissions, access controls, and monitoring are a must. - Learning Curve
While AI agents save time, getting comfortable with how they work (and what they're capable of) takes some learning.
Bottom line? Agentic AI is a powerful tool, but it's not a "set it and forget it" situation. With the right guardrails, though, it can save you time, stress, a whole lot of late nights, and provide tons of fun.
The Future of Agentic AI for DBAs and Developers
Agentic AI is not just a passing trend. It is truly the next big shift in how we work. For DBAs and developers, it's like having a tool that's not only smart but also proactive, handling problems before they turn into a nightmare.
In the near future, we'll see Agentic AI becoming a standard part of our workflows. For me, it has already made a huge difference.
For DBAs, it means moving away from constant, menial firefighting to focusing on big-picture strategy and architecture. For developers, it means more time for innovation instead of debugging or writing repetitive code.
The bottom line? I like to put it this way: AI will not take your job, but those using AI will. Agentic AI isn't here to replace us, but it could if we do not adjust, adapt, and better position ourselves to take advantage of its capabilities. Something I try not to forget.
Conclusion
Agentic AI is more than just another tool. It's a game-changer for us all. It tackles the repetitive, time-consuming tasks that eat up your day, so we can focus on the high-value work. From optimizing queries to troubleshooting issues and keeping systems secure, these AI-powered helpers are already proving their worth.
Happy Holidays! 🎄
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