Training vs Tuning: Why Managing AI Agents is an Ongoing Practice, Not a One-Time Project

AI agents aren’t finished once they’re trained. Unlike traditional software, they don’t stay static after deployment. They operate in changing environments, where user behavior, language, and business needs shift constantly.

If you do not keep changing your AI, it will be outdated quite fast. It is the reason why managing AI is an ongoing process. This article explains why tuning matters more than training after launch, and how to keep AI agents useful, accurate, and aligned with real-world needs.

The Myth of Completion: Why a Trained Agent Isn’t a Finished One

AI agents aren’t finished once you launch them. They keep learning from the world around them. After deployment, they start talking to real people, and that’s when things get tricky. People change how they ask questions. Recent problems come up. Business needs shift. In this way, AI adjustments become essential.

Legacy Software vs AI Agents

Traditional software follows a clear path: build, test, deploy, and maintain. Once deployed, it behaves the same way unless someone updates the code. AI agents don’t work like that. They rely on data and patterns, not fixed rules. Their performance depends on the environment they operate in, and that environment constantly changes.

Real-World Shift: What Happens After Deployment

Once you deploy an AI agent, it starts facing new inputs. Users ask different questions. Language evolves. Business needs shift. Over time, the model may stop performing as well. This is called concept drift. A 2023 study on model drift showed that even minor changes in input data can reduce accuracy if the model isn’t updated regularly. That’s why tuning after deployment becomes essential.

Training vs Tuning: Two Very Different Lifecycles

Training builds the foundation, while tuning shapes how the AI performs in the real world.

Training: Building the Brain

Training is where it all starts. It’s the process of teaching an AI model how to understand language, patterns, and general knowledge. This step uses massive amounts of data and computing power. It takes time and resources, and it usually happens only once or a few times. Training builds the brain, but not the job skills.

Tuning: Teaching the Job

Tuning comes after training. The areas to focus on are answering questions, helping customers, or authoring emails. It is better to refer to real-world examples and feedback to shape how the AI responds. It’s faster and more focused than training, and it helps the AI fit your business needs. This is especially important for AI customer service automation software, where accuracy and tone must match real user expectations.

Why Tuning Is Ongoing

Tuning doesn’t stop after launch. It’s something you do again and again. As users ask new questions or your business changes, the AI needs to keep up. Tuning helps the AI stay accurate, helpful, and aligned with what people actually need. This is where most of the value comes from keeping the AI useful over time.

Feedback is Fuel: Why Continuous Input Matters

Let’s be real – AI doesn’t magically get better just because it’s out in the wild. It needs help. And that help comes from feedback. Whether it’s a customer clicking “not helpful,” a support agent correcting a response, or someone flagging a weird answer, every bit of input teaches the AI how to do better next time.

Human-in-the-Loop Systems

This is where human-in-the-loop (HITL) systems come in. Think of it like coaching. You wouldn’t expect a new hire to crush it on day one without some guidance, right? The same goes for AI. AI makes this easy by baking feedback right into the workflow. If the AI gets something wrong, the system learns from it adjusting tone, improving how it understands questions, and knowing when to pass things off to a human. It’s like giving your AI a performance review… every day.

The Role of Prompt Tuning, RAG, and Lightweight Updates

Now, here’s the cool part: you don’t need to retrain the whole model every time it messes up. With things like prompt tuning and RAG (retrieval-augmented generation), you can make quick fixes without starting from scratch. It’s like updating your playlist instead of rewriting the whole album. GitHub Copilot and Salesforce Einstein do this all the time. They learn from how people use them and get better with every click.

And hey, not every company gets this right. In 2025, Klarna tried to go all-in on AI and cut too much human support. Spoiler: it didn’t go well. Service quality dropped, and they had to bring people back in. Lesson learned? Feedback is what keeps your AI from going off the rails.

Tuning as Product Ops, Not Just ML Work 

Tuning isn’t just a job for machine learning engineers anymore. It’s a team sport. Product managers define what success looks like. Engineers make the changes. Data teams track what’s working and what’s not. In companies using AI customer service automation software, this kind of cross-functional ownership is what keeps things running smoothly. CoSupport AI, for example, treats tuning like part of product operations, because that’s exactly what it is.

Final Thoughts: Cross-Functional AI Is the Future of Customer Experience

People don’t care what team they’re talking to. They just want help. Fast, clear, and without having to explain themselves twice.

That’s why smart handoffs matter. It is about making sure the interaction flows, even when a bot requires to switch roles – from onboarding to troubleshooting or from support to sales. If the handoff is smooth, the customer barely notices. If it’s not, they feel it right away.

I’ve seen companies lose customers over something as small as a bot asking the same question twice. And I’ve seen others build real loyalty just by making things feel easy. The difference? Thoughtful design. AI that knows when to switch, what to carry over, and when to bring in a human.

Here’s the takeaway: don’t just build bots that answer questions. Build ones that understand the journey.