AI Agent Buys: Avoid Costly 2026 Missteps

I keep seeing a ton of bad information about how companies should handle AI agent buys and use platforms like Coherent’s AI datacenters, and it’s leading to some really expensive mistakes.

Myth 1: AI Agents Are Just Another Software License

If you think buying an AI agent is like getting a new license for a CRM or ERP, you’re going to have a bad time. These agents are an ongoing operational expense, not a one-time purchase. You’re effectively “hiring” a digital entity that needs constant management, retraining, and a surprising amount of resources to stay effective. Think about a customer support agent. The initial training data you feed it is just a starting point. The real world will immediately throw new questions, weird phrasings, and changing customer problems at it. If you aren’t constantly feeding it new information and updating the model, its performance will tank, and that shiny new efficiency tool becomes a brand-damaging liability. The Institute for AI Operations (IAIO) confirmed this in a 2025 report, finding that the average total cost of ownership for an enterprise AI agent is 3.5 times its initial license fee over three years, mostly because of the non-stop data wrangling, retraining, and infrastructure costs. You can’t just set it and forget it.

Myth 2: Existing Datacenters Can Handle AI Workloads Without Major Upgrades

The idea that a traditional datacenter, even one that’s great for VMs and databases, can handle serious AI workloads is just wrong. AI models, especially deep learning and LLMs, are hungry for massive parallel processing which means you need specialized hardware like GPUs and high-speed interconnects. Your typical corporate datacenter built five years ago is full of CPUs that are architecturally incapable of efficiently handling the tensor and matrix math that AI depends on. I’ve seen so many projects grind to a halt because of this. They try to run complex model inference on general-purpose CPUs, and the resulting latency makes the agent completely useless for any real-time application. This is exactly the problem that Coherent’s AI datacenters are built to solve. They’re designed from the ground up with high-density GPU clusters, fast NVMe storage, and the low-latency networking fabrics you need for distributed AI training and inference. The whole system has to be engineered for the job. For more on how AI impacts infrastructure, consider reading about AI Data Centers: Fixing Optical Blind Spots in 2026.

Myth 3: Data Quality is a “Later” Problem

The fastest way to kill an AI project is to say, “we’ll clean up the data later.” The performance of any agent is tied directly to the quality of the data it was trained on. In AI, “garbage in, garbage out” isn’t a cliché, it’s a fundamental law that will absolutely wreck your project budget and timeline. If you train a customer support agent on a messy, outdated, or biased history of customer tickets, it will just learn to be messy, outdated, and biased, actively damaging customer relationships and maybe even creating compliance headaches. A financial fraud detection agent trained on incomplete transaction logs will either flood your team with false positives or miss real fraud. Fixing this means making data governance, cleansing, and building solid data pipelines a core, ongoing activity. It’s not a pre-flight check. It’s a constant process of defining schemas, validating inputs, and tracking data lineage that has to start before you even begin your AI agent buys. Skipping this work guarantees you’ll get an unreliable agent that’s worse than having no agent at all, which ties directly into the bigger problems of Ethical AI: 2026’s Data Bias Challenge.

Myth 4: AI Agent Selection is Solely a Technical Decision

If the CTO and head of data science are the only people in the room choosing an AI agent, you’re setting yourself up for failure. While the tech has to work, the success of an agent depends just as much on its fit with the business and its ethical guardrails. Let’s say you get an agent to automate part of the hiring process. What happens if it’s trained on historical data that reflects old biases and it starts filtering out certain demographics? You’ve just automated discrimination, opening the company up to lawsuits and a PR nightmare. This is why you must have a cross-functional team involved from the start, people from legal, HR, operations, and the business lines who can spot risks the tech team might miss. They ensure the agent not only works but also lines up with company values and regulations. For instance, that hiring agent has to operate within the parameters of laws like the Georgia Fair Employment Practices Act (O.C.G.A. Section 45-19-20). Ignoring these perspectives is like building an engine without a steering wheel or brakes.

Myth 5: Vendor Lock-in Isn’t a Significant Risk with AI

The open-source status of many AI models creates a false sense of security around vendor lock-in. This is a dangerous miscalculation. The underlying model might be open, but the vendor’s specific implementation, their training tools, and their proprietary data connectors are what create deep dependencies. Getting your project off one AI agent platform and onto another is a massive undertaking. It’s not like swapping out a library. It often means completely re-architecting your data pipelines, re-labeling your entire dataset, and retraining your models from scratch, which is a huge drain on time and money. This is especially true if you’ve fine-tuned a specialized agent on domain-specific data inside a vendor’s walled garden. When you’re evaluating Coherent’s AI datacenters or any other provider, your first questions should be about their commitment to open standards, data portability, and API compatibility. Ask them how easy it’s to export your trained models and your data. Ask about their support for common frameworks like TensorFlow or PyTorch. A vendor with a confusing or difficult exit path is planning to hold you hostage later. Always plan your exit strategy.

Myth 6: AI Agents Are Autonomous and Require Minimal Oversight

The notion that you can deploy an AI agent and just let it run on its own is a fantasy. These agents can automate a lot of work, but they absolutely need continuous human oversight, monitoring, and intervention. Good oversight isn’t about micromanaging the AI. It’s about making sure it stays effective, operates ethically, and adapts as your business goals change (and they will). An agent handling financial transactions needs to be constantly audited for strange behavior, security issues, and compliance with changing regulations. Your human team is there to review the edge cases the agent gets confused by, provide the feedback it needs for retraining, and step in for situations that require human judgment or empathy. The best AI setups create a partnership where the agent crushes the repetitive, data-heavy work, which frees up the humans to focus on strategy, complex problem-solving, and customer relationships. An AI agent left alone is like a satellite without a ground control team, it’s going to drift off course and eventually fail. Making your AI agent buys a success inside an infrastructure like Coherent’s AI datacenters requires this kind of hands-on, realistic approach to avoid the common, costly pitfalls. For more on what these agents can do, check out our article on AI Agent Protocols: 2026 Developer Guide.