AI Agents Step Up — Not To Replace People, But to Amplify Real Work

A subtle but meaningful shift in how autonomous AI “agents” are starting to carry out useful tasks — and why business leaders should pay attention now.

What Happened

Mollick draws attention to experiments showing that AI agents are now doing more than just responding to prompts—they’re autonomously coordinating among themselves, taking on specialized roles, and handling sequences of tasks with little human intervention. For example, in one “guessing game” experiment he describes, agents developed patterns of collaboration and role-division without being told to do so. (LinkedIn) Meanwhile, he reflects on academic work showing that these agents are crossing a threshold: they might not replace entire jobs, but they are starting to be capable of performing parts of jobs in ways that deliver real value. (LinkedIn)

A related post points out a practical gap: many current agent systems fail because they don’t ask enough clarifying questions. Without those questions, the agent risks taking a “best-guess” route that may not match the business need. (LinkedIn)

Why This Matters

For business leaders, the takeaway is that “agent technology” (automated software that plans and executes tasks) is reaching useful maturity. The breakthrough isn’t “AI replacing human workers wholesale,” but “AI becoming a capable assistant that handles tasks in sequences, coordinating across steps, freeing humans to focus on higher-value work.” Mollick is careful to stress that: these are not full job replacements but instruments of productivity. (LinkedIn)

Moreover, the fact that agents are starting to ask intelligent questions—or at least that their designers are recognizing the need for them—signals that organizations building or buying these tools must think: “Are these agents equipped to clarify, iterate, escalate when needed?” Instead of just “plug and play.” (LinkedIn)

Practical Takeaway

If you’re leading or advising an organization, here’s what to keep in mind:

Start by identifying task chains where the steps are relatively well-defined, repeatable, and lower risk. These are the sweet spots for agent deployment (rather than full strategic decision-making).
Ask vendors (or your own team) how the agent handles uncertainty: Does it ask questions? Does it escalate when unsure? Or does it simply execute guessing routines?
Integrate agents into workflows as complements, not as one-to-one replacements. Let humans define meaning, validate outcomes, and manage exceptions; let agents do the routine “move/execute/coordinate” work.

Reflection: What This Signals About AI’s Direction

We’re moving into a phase where autonomous agents aren’t simply amusement or hype—they are becoming viable operational tools. The business frontier is shifting from “Can agents ever do useful work?” to “Which work should we let them do, and how do we embed them responsibly?” As Mollick’s research underscores, the magnitude of value comes not from replacing people but from enabling new combinations of human-plus-agent workflows. (LinkedIn)

For business leaders: the focus should now shift toward governance, task design, and agent-human interaction. Before investing heavily in “fully autonomous” agents, make sure your organization understands how tasks are structured, when to trigger human oversight, and how to design agents that ask, listen, escalate. In short: intelligence isn’t just about the model—it’s about the context and workflow.

In the near future, the competitive edge may favor companies that master the coordination of agents and humans, rather than those who merely deploy isolated model features.