Getting Real About AI Agents and Why This Conversation Matters

AI Agents are terribly misunderstood and oversold. There have been promises, hype, and commercials about how AI agents are going to change the way people do business forever. Which they very well might. With that being said, they don’t deliver value simply because they exist or because they’ve been plugged into a system.

Even the definition of an AI agent shifts depending on who you’re talking to. Vendors often describe them as interchangeable building blocks. Leadership hears language about autonomy, scale, and efficiency. Teams closest to the work are left trying to figure out what, exactly, is being added to their day-to-day responsibilities and how it’s supposed to help.

“AI agent” has become a convenient (and honestly annoying) catch-all that glosses over meaningful differences in capability, control, and risk. When everything gets labeled an agent, it becomes harder to ask the right questions, evaluate tradeoffs, or set realistic expectations. Organizations end up buying or building systems they don’t fully understand, then scrambling to explain why the results don’t line up with what was promised.

This is important because AI agents can create real, measurable value when they are designed with intention. THEY SHOULD BE BORING. They fail just as reliably when the technology is vague, oversized, or disconnected from how work happens. Getting value from agents requires precision. It requires discipline. And it requires a clear understanding of what problem is being solved, what role the agent plays, and what boundaries it needs to operate effectively.

Getting Precise About What You’re Buying or Building

That need for precision starts with the language itself.

One of the biggest issues in the AI agent conversation is that the term is doing far too much work. It gets applied to everything from chatbots and copilots to machine learning models and autonomous process controls. When that happens, teams lose the ability to clearly evaluate what they are actually buying or building.

Clarity at this stage is simply about setting the right expectations. Generative AI designed to create or summarize content behaves very differently from machine learning models focused on prediction and variance detection. Rules-based agents operating within defined thresholds behave differently still. Each comes with its own strengths, constraints, and failure modes, and treating them as interchangeable creates confusion before any value is realized.

Many offerings marketed as “AI agents” are effectively GPT-based interfaces wrapped in a workflow. That does not make them useless. It does mean organizations need to understand what sits underneath the label. What model is driving decisions. Where the data lives. How context is constrained. What happens when outputs fall outside acceptable bounds.

The value here comes from reducing ambiguity early. When teams understand what they are working with, they can make informed decisions about risk, governance, and fit. That precision builds confidence across the organization and creates space to move forward thoughtfully, without overpromising at the outset or overcorrecting later.

Tying Agents to Clear Value Through Narrow, Task-Based Design

Once there is clarity around what kind of AI is in play, attention naturally shifts to how it should be used. The goal is to stay anchored to the problem the organization is trying to solve. Don’t get caught up in the list of things that are “possible”. Big thinking is great as long as we are grounded in reality.

AI agents deliver the most value when they are tied to a specific point of friction in the business. Bottlenecks in throughput. Excess work sitting between process steps. Timing mismatches in supply chains. Areas where variability forces teams into constant reaction. These are the places where an agent can meaningfully support better decisions, not by replacing judgment, but by reducing noise.

Problems arise when organizations try to solve everything at once. Broad, general-purpose agents may sound appealing, but they are difficult to govern and even harder to trust. A more effective approach is to start small and be intentional. Choose a narrow use case, understand the value it should create, and design the agent to do one job well.

In practice, that means designing agents as part of a workflow rather than as standalone solutions. One agent observes conditions. Another determines whether thresholds have been crossed. Another recommends or executes an action. Breaking work into these discrete steps keeps context focused and behavior predictable.

This structure has a direct impact on outcomes. Narrow scope improves reliability, reduces unexpected behavior, and makes it easier to understand whether the agent is actually delivering value. Over time, these targeted wins can be connected into a roadmap that expands capability without sacrificing control or clarity.

Building on a Systems and Data Foundation That Can Support Agents

As agents move from isolated use cases into active operations, a new reality sets in. Nothing exists in isolation. Processes are connected, and changes in one area inevitably affect others. Improving throughput influences supply chain timing. Reducing variability in one step reshapes behavior downstream. Agents make these connections more visible and more immediate, which raises the stakes of how they are designed and governed.

This is where a systems view becomes non-negotiable. Agents do not need perfect processes or pristine data to be useful, but they do require a level of stability. Work has to be consistent enough to observe, and data reliable enough to guide decisions. Without that baseline, agents accelerate the same inconsistencies teams have been working around for years.

Guardrails play a central role in maintaining that stability. Clear thresholds define what acceptable variation looks like. Escalation paths determine when human intervention is required. Oversight ensures that decisions remain grounded in context. These boundaries allow agents to support better decision-making without removing accountability or judgment.

Just as important is the human side of the equation. For agents to be effective over time, the people closest to the work need to trust them. That trust is built when agents behave predictably, align with how work actually gets done, and reinforce existing expertise rather than override it. When teams see agents as support instead of disruption, adoption becomes far more natural.

The value here shows up in longevity. Agents stop being experimental tools and start becoming part of how the organization improves. Instead of fighting the system or constantly reworking it, teams learn alongside it, refining both the process and the technology as conditions change.

Tying It All Together

AI agents earn their value through clarity and restraint. When organizations are precise about what they are building, deliberate about where it fits, and realistic about what it can and cannot do, agents become steady contributors rather than sources of friction. They work best when they are tied to real business outcomes and supported by processes and data that are stable enough to guide, not guess.

This conversation is not about chasing the newest capability or keeping up with the latest narrative. It is about designing systems that can absorb complexity without losing control. When that foundation is in place, AI agents stop feeling abstract or experimental. They become practical tools that reinforce how the business operates, helping teams make better decisions with more confidence as conditions change.

If you have opinions, pushback, or questions, I’d genuinely love to hear them. This is one of those topics where the nuance matters, and the best conversations tend to happen outside the hype cycle.

If something here sparked a thought or raised a question, feel free to reach out and continue the conversation.

See you next time,

-Kyler