Agentic AI has moved well beyond being a theoretical concept or experimental tool. The conversation is no longer about whether intelligent agents are possible, but about how they are actually being used, where they deliver real value, and what is preventing broader adoption.
Experts from a recent Tech in Motion Webinar painted a realistic picture of how agentic AI is taking shape in the tech world in 2026. Across the discussion, five major themes emerged that are key to understanding the future.
“The thing that the agentic workflow really excels with is dealing with those unanticipated, unexpected conditions… addressing them in real time as opposed to in days or weeks or being missed entirely.
BARRY REIMER, Motion Telco
From Automation to Intelligent Autonomy
Classic automation works well when the rules are stable and predictable. Agentic AI, by contrast, is designed for environments where conditions change constantly and where perfect data rarely exists. The shift is not about replacing humans with machines, but about building systems that can adapt when things do not go according to plan.
This is particularly relevant in complex operational environments found in telecom, healthcare, finance, and other industries, where legacy IT infrastructure coexists with modern platforms.
Agentic systems can monitor data flows, identify anomalies, route tasks, and escalate issues dynamically instead of waiting for human intervention. The real breakthrough is not speed alone, but responsiveness. These systems can handle edge cases and uncertainty, where most real-world failures occur.
What makes this trend significant is that it reframes how organizations think about automation. Instead of designing rigid workflows, teams are beginning to design flexible systems that learn, adapt, and collaborate with humans in real time.
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Host David Yakobovich discusses how Agentic AI is reshaping tech in 2026 with fellow panelists Shuki Licht, Barry Reimer, and Wade Erickson
Integration Beats Replacement
AI is not replacing existing systems but wrapping around them. The most successful implementations are not radical rebuilds, but layered architectures that connect AI to what already exists.
Instead of discarding established workflows, organizations are inserting agents into specific points of friction. Claims processing, customer service triage, IT incident management, and document handling are common examples.
In these models, agents act as intelligent intermediaries. They observe data, make recommendations, perform routine actions when confidence is high, and hand control back to humans when uncertainty increases. Over time, the system learns from human decisions and gradually expands its scope.
The importance of this trend lies in its realism. It acknowledges that most organizations cannot afford to start from scratch. Agentic AI succeeds when it respects existing processes while quietly improving them.
We are seeing mostly how to adapt and integrate pieces of AI into the legacy system and into existing flow.”
SHUKI LICHT, HEALTH EQUITY
Non-Technical Users as Agent Builders
Agentic AI is no longer confined to engineers or data scientists. No-code and low-code tools are enabling business users, marketers, analysts, and even students to build useful agents without writing traditional software.
This represents a fundamental change in how technology spreads within organizations. In previous generations, innovation flowed from IT departments downward.
With agentic AI, it increasingly flows from individuals upward. People start by solving their own problems, automating their own workflows, and building personal agents that save time. Only later do those ideas get formalized into enterprise systems.
This bottom-up pattern mirrors what happened with spreadsheets, websites, and mobile apps. Tools that begin as personal productivity hacks eventually become organizational infrastructure. The difference now is speed. What used to take months of development can be achieved in hours.
The cultural impact may be as important as the technical one. As more people learn to think in terms of delegating tasks to agents, a new mindset emerges. Instead of asking “How do I do this?” people ask “How do I get an agent to do this for me?
Not everyone has to build agents. On a team of ten, a few can build, and the rest use them."
Wade Erickson, Motion Consulting Group
New Risks. New Thinking
Agentic AI introduces a new category of threats that go beyond traditional cybersecurity. When systems can act, delegate, and even create sub-agents, mistakes become amplified.
The risks fall into two categories. The first is operational risk: agents performing unintended actions due to flawed prompts, bad data, or misunderstood goals.
The second is strategic risk: malicious agents exploiting systems, discovering vulnerabilities, and adapting faster than human defenders can respond.
What makes these risks unique is their autonomy. Traditional software executes instructions; agentic systems interpret goals. That difference changes the nature of accountability. And if something goes wrong, responsibility becomes harder to trace.
The response is not to slow down innovation, but to embed thoughtful and well-constructed safeguards directly into system design.
You might cross that line with providing feedback and giving over autonomy and not even realize that you’ve done that.”
BARRY REIMER, MOTION TELCO
Human-in-the-Loop is Essential
The challenge with agentic systems is not raw intelligence, but trust. AI outputs can be impressive yet fragile. Without strong validation, observability, and confidence thresholds, systems degrade in production quickly. Drift, hallucinations, and edge cases are inevitable.
Successful teams design explicit rules about when agents areallowed to act and when they must defer. When an agent is unsure, it escalates. When it is confident, it proceeds. Every human decision becomes new training data for future cases.
This approach creates a feedback loop rather than a replacement loop. AI improves by learning from human judgment, not by eliminating it.
Human-in-the-loop is not a temporary safety measure; it is a permanent design principle.
If I don’t have 99% of confidence, and I’m not having a guard that really validates results, just drop it to a human.”
SHUKI LICHT, HEALTH EQUITY
