"Agentic AI" Is the New Buzzword—Is it the Future or Just Another Fad?
Five Principles For Deploying Agentic AI And Maintaining Your Values
There’s a new buzz term making the rounds: Agentic AI.
Depending on who you ask, it’s either a revolution in how work gets done—or just another shiny rebrand of automation. But behind the hype is a real shift worth paying attention to.
We’re talking about AI that doesn’t just wait for instructions—it takes action. It follows up, resolves issues, even makes decisions inside your business. And that changes things. Not just for efficiency, but for how organizations function, how people contribute, and what leaders need to watch for.
In this week’s newsletter, I unpack what Agentic AI really is, what’s being overhyped, and how to harness its potential without sidelining the humans it’s meant to help.
And you can also lear about AI my podcast, Humanity At Scale: Redefining Leadership, as it is a recurring topic across many episodes.
Here are some interesting snippets:
True understanding doesn’t come from AI. Vivienne Ming, theoretical neuroscientist and AI entrepreneur, emphasizes that while AI can generate answers, it can’t understand problems—leaders must ask better questions to get better results.
Leaders must teach AI how to reflect values. Kurt Gray, moral psychologist and professor at UNC Chapel Hill, argues that AI models can only act on behalf of humans if we first articulate and expose the reasoning behind our own decisions.
Using AI without governance is risky. Renee Cacchillo, CEO of Safelite AutoGlass, stresses the importance of auditing and governing AI tools—treating them with the same care as cybersecurity systems to ensure they support, not disrupt, customer trust.
I hope this content encourages you to think not just about what AI can do, but about what it should do—and how your leadership shapes that path.
Let’s jump in.
Agentic AI: The Hype, The Reality, And The Playbook
A new AI buzzword is spreading fast—and it’s leaving people with more questions than answers. “Agentic AI.”
Is it a real breakthrough? Just a rebrand of automation? Or something in between?
Most leaders I talk to aren’t sure. But one thing is clear: if AI is going to act for us, we need to understand what that means—including what could go wrong.
In this piece, I’ll cut through the buzz, explain what agentic AI actually means, and explore how to get the most out of it—without sidelining the people it’s meant to support.
What Agentic AI Really Means
At its core, Agentic AI is about giving AI systems more independence. Instead of waiting for commands or responding to prompts, these systems take action to move toward a goal. They don’t just answer questions—they follow through.
For example:
Imagine an AI that runs your onboarding process. It reaches out to stakeholders, tracks down missing documents, schedules check-ins, and follows up—without being told to do each step.
Or picture a customer service agent that troubleshoots an issue, pulls in data from other tools, and even offers a refund if needed.
These systems behave more like actors than assistants. They move from being tools we use to agents that operate inside our organizations or on our personal behalf. And that’s not a small upgrade. It’s a redefinition of how work gets done—and who (or what) is doing it.
And that’s why leadership matters. Because as these systems gain agency, they also need intentional design. If we don’t shape how they act, they’ll still act—just not always in ways that serve people, culture, or outcomes.
The Value of Agentic AI
Agentic AI is more than hype. The combination of autonomous workflows and intelligence will change how organizations operate. When thoughtfully deployed, it can:
Improve agility. Agents can adapt quickly to changing inputs—whether it’s shifting customer needs, internal goals, or operational disruptions—helping organizations stay responsive in dynamic environments.
Strengthen coordination. By working across systems and silos, agents can streamline handoffs, flag breakdowns, and help teams stay better aligned around shared objectives.
Increase efficiency. Agents reduce manual effort by handling repetitive or time-consuming tasks—freeing up time, reducing friction, and speeding up workflows.
Reinforce shared values. When built with purpose, agents can carry out decisions that reflect what the organization stands for—amplifying trust, consistency, and cultural alignment. When agents handle repetitive tasks, humans can spend more time on creativity, connection, and problem-solving.
What’s Overhyped? Effort And Timing
Agentic AI is stirring up excitement—and with it, a wave of unrealistic expectations. The problem isn’t just that the hype is premature. It’s that it glosses over how much effort is required to make these systems truly work in the messy reality of business.
The idea of fully autonomous AI agents sounds appealing. But in practice, most deployments fall short—not because the tools aren’t capable, but because the environments they operate in aren’t ready. Building Agentic AI that’s useful, responsible, and aligned with human needs means doing the hard work of integration. That includes threading agents into the organization’s data architecture, business logic, and operational workflows—something far more complex than dropping in a chatbot or assigning a task bot.
Let’s unpack a few of the most common misconceptions:
“The AI can manage itself.” Not exactly. These systems can follow steps and pursue goals—but they don’t know your values, edge cases, or cultural norms unless you explicitly teach them.
“We can replace entire departments with agents.” In narrow, well-defined processes, maybe. But most real-world work is cross-functional, exception-heavy, and full of emotional nuance. Agents aren’t built for that—yet.
“It’s like hiring another team member.” It’s more like hiring a tireless, literal-minded intern. Fast and consistent, yes. But without judgment, empathy, or awareness of what’s unsaid.
The takeaway? Agentic AI isn’t plug-and-play. It needs to be trained, tuned, and deeply embedded into your systems. That includes connecting to data sources, orchestrating across tools, and aligning to how work really flows. And all of that has to be shaped around human needs—not just machine logic.
Five Principles for Deploying Agentic AI
It’s easy to get excited about what Agentic AI can do. But the bigger question is what it might do—on its own, at scale, and in ways you didn’t intend. These systems don’t just automate tasks. They interpret goals, make decisions, and take action—without waiting for permission.
That’s why leaders need a robust blueprint for shaping how these systems behave—and how they serve the organization. Here are five principles for deploying Agentic AI—each designed to help you stay grounded in human impact while you scale digital decision-making:
Define Ultimate Goals
Establish Clear Boundaries
Escalate When Uncertain
Make Decisions Traceable
Preserve Human Agency
Principle 1: Define Ultimate Goals
Agentic AI is relentless—it doesn’t get tired, bored, or distracted. Once it has a goal, it will pursue it with precision. But it doesn’t know what you meant, only what you told it to do. That gap between intention and instruction is where things go sideways. The agent might achieve the metric—but miss the point.
This principle is about defining the objectives agents optimize for—and making sure those objectives reflect the real outcomes your organization values. When goals are misaligned, agents deliver performance that looks right on paper but feels wrong in practice. Good intentions don’t matter if they aren’t translated into the system’s definition of success.
Design: Choose goals that reflect what good actually looks like. Agents will optimize exactly what you tell them to—even if that optimization undermines trust or experience. That’s why design starts with defining what success really means in human terms. If a service agent is told to minimize call time, it may rush through interactions and frustrate customers. But if it’s optimized for “first-call resolution with high customer confidence,” it’s more likely to take the time to explain and solve the real issue. The goal becomes the culture of the interaction.
Establish: Translate those goals into how agents behave. It’s not enough to state the desired outcome—the system has to embody it in how it acts. That means building incentives, constraints, and training data that reward the right trade-offs. For instance, if your intent is to create inclusive hiring, your recruiting agent shouldn’t just optimize for speed or conversion—it should be guided to look for signs of diverse experience or mitigate for bias. The logic behind the goal must be present in how the agent navigates gray areas, not just clear-cut decisions.
Evolve: Recalibrate goals when behavior drifts from intent. When agents deliver results that technically meet the target but feel misaligned with your purpose, it’s a signal to revise the goal itself—not just the surrounding process. Start by examining how the agent is interpreting success: what actions is it prioritizing, and what is it ignoring? Then refine the goal definition, retrain with updated examples, or adjust the logic to reward better behaviors. If a support agent closes tickets quickly but lowers satisfaction, re-weight its loop to favor resolution confidence. Goal alignment isn’t set-it-and-forget-it—it’s a discipline of continuous refinement.
Principle 2: Establish Clear Boundaries
Agentic AI systems are autonomous by design—they pursue goals and make decisions without constant human input. That can unlock speed and scale, but it also creates exposure: when boundaries are vague, agents can take actions that were never authorized—triggering compliance gaps, creating confusion, or damaging trust.
This principle is about defining the outer limits of an agent’s authority: what it is allowed to do, where it can operate, which systems it can access, and when it must stop or escalate to a human. Boundaries don’t constrain value—they protect it. They provide a behavioral perimeter that lets the system act with clarity, without crossing into areas where it doesn’t belong.
Design: Define the edge of authorized autonomy. Designing boundaries starts with clarifying what kind of authority the agent is being granted—and where that authority ends. That includes specifying what actions it can take, what decisions it can make without review, what systems it can access, and which business rules govern its behavior. These inputs should reflect organizational structure, not just technical capability. When this perimeter is unclear, agents make choices they were never meant to own. The goal is to make the edges of autonomy sharp—not vague or implied.
Establish: Operationalize functional, contextual, and hierarchical limits. Once boundaries are defined, they must be embedded into the agent’s architecture and workflows. That includes functional boundaries (what the agent can do), contextual boundaries (when it’s appropriate to act), and hierarchical boundaries (which decisions require elevated review). For example, an agent may automatically schedule interviews for junior roles, but escalate executive hiring requests. These limits should be enforced through permissions, escalation paths, and fail-safes—so agents don’t just execute correctly, but behave appropriately within their assigned domain.
Evolve: Monitor for scope drift and hidden expansion. Boundaries don’t break loudly—they erode quietly. A team starts using the agent for adjacent tasks. A new integration widens access. The agent’s role slowly expands without anyone noticing. These changes often emerge through informal use, not technical failure. That’s why boundary management must be treated as an ongoing leadership responsibility. Audit the agent’s behavior regularly. Revisit scope when roles, systems, or policies shift. The most trustworthy agents aren’t the ones that do the most—they’re the ones that know exactly where they stop.
Principle 3: Escalate When Uncertain
Agentic AI systems are designed to act—but action without confidence can backfire. An agent might move forward on a shaky signal, interpret an edge case as routine, or make a decision it was never meant to own. That’s where escalation matters. It’s not a sign of failure—it’s a form of intelligence.
This principle is about recognizing the limits of certainty and ensuring agents know when to pause, seek help, or escalate a situation to a human. When agents don’t know how to handle ambiguity, they either do nothing—or worse, the wrong thing. Escalation isn’t just about handing off problems. It’s about maintaining trust by making sure the right mind is on the task when judgment is required.
Design: Define thresholds for uncertainty, not just failure. Most systems are designed to detect when something breaks. But agentic systems also need to detect when something feels off. That might mean low confidence in a classification, unusual combinations of signals, or tone mismatches in human interaction. For example, if a talent agent sees a resignation come through just days after poor pulse survey results, it shouldn’t just process the exit—it should flag a pattern. These thresholds should be set to capture ambiguity, not just technical error.
Establish: Build escalation into workflows—not around them. Escalation can’t be bolted on after the fact. It must be designed into how the agent operates, including who it defers to, how handoffs are structured, and what context is passed along. If a frontline service agent encounters an emotional customer, it should not only pause action—it should notify a human with enough background to respond meaningfully. Without this structure, escalation becomes abandonment. With it, it becomes a bridge between machine action and human care.
Evolve: Tune escalation logic based on impact and patterns. Escalation rules should adapt over time—not just based on volume, but on value. Leaders should monitor when escalations are triggered, how they’re resolved, and whether earlier intervention could have helped. If a recruiting agent frequently escalates candidate conversations flagged as inconsistent or ambiguous, that may signal a need for more context or better intent recognition. If legal escalations are always rubber-stamped, perhaps the agent can own more. Escalation is a signal of design maturity—not a weakness, but a checkpoint for refinement.
Principle 4: Make Decisions Traceable
Agents may act independently, but their choices shouldn’t be opaque. If people can’t see how or why a decision was made, they’ll struggle to trust it—or improve it. That’s especially true when agents operate behind the scenes or make rapid decisions at scale. Traceability is what turns automation into accountability.
This principle is about ensuring that every agentic action can be explained, audited, and understood by the humans who rely on it. Without a clear view into how decisions are made, errors can’t be corrected, bias can’t be surfaced, and accountability fades. Transparency isn't just for compliance—it's foundational for trust.
Design: Treat explainability as a core design input. Building for traceability means designing systems that don’t just output decisions, but surface the logic behind them. That includes keeping a record of what data was used, what rules or models were applied, and what alternatives were considered. For instance, if a finance agent flags a suspicious transaction, it should explain which patterns triggered concern—not just give a risk score. Traceability should be part of the design spec, not an afterthought.
Establish: Build interfaces that reveal, not obscure. Users shouldn’t need a data science degree to understand why an agent did what it did. That’s why explainability must show up in how people interact with the system. That might include step-by-step breakdowns, confidence scores, or visualizations of decision paths. For example, a talent agent recommending promotions should display not just qualifications but the factors that carried the most weight. The goal isn’t just to show the outcome—it’s to help users make sense of it.
Evolve: Review traceability as decisions grow more complex. As agents are used for more nuanced or impactful tasks, their reasoning may become harder to follow. Leaders should periodically test whether users can still interpret the system’s choices, especially in edge cases. When audit trails start to feel like black boxes—or when people stop asking questions—that’s a sign transparency is breaking down. Traceability must scale with capability. If people can’t interrogate the system, they’ll eventually stop trusting it.
Principle 5: Preserve Human Agency
Agentic AI can lighten the load, streamline work, and automate decisions—but when it starts making choices on your behalf without visibility or control, something deeper gets lost: your ability to shape outcomes. When people feel displaced by automation instead of empowered by it, resistance grows, trust erodes, and performance declines.
This principle is about ensuring that humans remain meaningfully in control—able to oversee, influence, or override the actions of an agent when needed. It’s not enough for systems to function correctly. They must function in ways that reinforce the user’s sense of ownership and participation.
Design: Make space for meaningful human input. Systems should be designed so that people can guide, modify, or redirect the agent’s behavior in ways that matter. That includes setting preferences, nudging decisions, or reviewing recommendations. For instance, a manager using a performance review agent should be able to adjust language or add nuance—not just accept or reject a generated summary. Design choices should avoid “locked-in” automation that excludes context or discretion.
Establish: Reinforce control through visibility and interaction. To feel agency, users need to see what the system is doing and have accessible ways to shape or stop it. That includes audit trails, decision explanations, and lightweight controls. A scheduling agent, for example, should surface the logic behind a meeting it proposed—why those times, why that group—and offer a one-click override. When agents operate in ways that respect your organization's cultural tone and rhythms—such as how decisions are communicated or who is informed first—it reinforces trust, not just efficiency.
Evolve: Monitor for passive use and declining ownership. Over time, users may start letting the system make all the decisions—even when it shouldn’t. That’s not just convenience; it’s a potential erosion of judgment. Leaders should track when and how people intervene, solicit feedback on whether they feel sidelined, and recalibrate controls when engagement drops. If a compliance agent flags issues that are always ignored or always accepted, it may signal that users no longer feel accountable. Preserving agency means designing for participation—not just performance.
Sparking New Leadership Thinking
As you deploy Agentic AI and apply the five principles I’ve laid out, leaders will need to change some of their efforts and focus. Here are five new actions to consider:
Assign new agents a senior-level “buddy.” In the early phases of deployment, treat agents like apprentices—not autonomous operators. Pair each one with a senior leader or manager responsible for observing how it behaves, identifying edge cases it mishandles, and documenting where its decisions diverge from human judgment. This creates accountability and builds shared learning before scaling.
Redefine KPIs with both outcomes and experiences. Don’t let agents optimize only for operational wins. For any agent deployed—like in customer service or recruiting—update its core KPIs to include human signals. Example: pair “time to resolution” with “net positive sentiment in post-interaction survey.” Ensure your dashboards balance speed with emotional impact.
Conduct quarterly leadership reviews of agent deployment. Use a structured leadership session to review where agents are being used across the organization, what they’re optimizing for, and where friction or drift is emerging. This becomes the foundation for building an intentional roadmap: where agents add value, where guardrails need strengthening, and where human involvement remains essential.
Engage cross-functional teams in agent design. Don’t limit design input to technical teams or frontline users. Include people from legal, brand, HR, and operations in workshops to define how agents should behave in complex scenarios. Ask: “What would it mean for this agent to reflect our values in this moment?” Their judgment becomes part of the agent’s blueprint.
Create an AI Agent Advisory Council. Form a cross-functional group—spanning ops, tech, HR, CX, compliance, and frontline roles—to guide how AI agents are deployed. Their role isn’t to review code, but to ensure agents reflect company values, serve real human needs, and elevate people’s contributions—not sideline them. The council reviews deployments, flags risks, and advises on where agent logic needs refining to support trust, fairness, and connection.
The Bottom Line
Agentic AI enables systems to make decisions and take action across tasks—but it acts on goals and logic, not values or human judgment. That’s why leaders must treat it as a design challenge: shaping agents to reflect the organization’s purpose, align with its values, and know when to pause, escalate, or ask for help.
Additional Resources
Here’s some of my previous newsletters that discuss AI that you may find interesting:
Five Paths For AI & My First Podcast Drops. AI isn’t destiny—it’s a tool. In this edition, I explore whether it will fuel opportunity or cuts jobs, deepen connection or dehumanize, concentrate power or spread it, spark creativity or stifle it, exploit or uplift. humanity
10 Predictions For 2025: The Year Of AI, Simplicity, and Human Connection. In this edition, I share 10 predictions for 2025 that will reshape the way we work, lead, and connect.
Humanity At Scale: Redefining Leadership Podcast
Available on Apple Podcasts, Spotify, and YouTube.
Make sure to check out my podcast, where I reimagine leadership for today’s dynamic world—proving that true success begins with prioritizing people, including employees, customers, and the communities you serve. From candid conversations with executives to breakthrough insights from experts, Humanity at Scale: Redefining Leadership Podcast is your ultimate guide to leading with purpose and empathy.
Here are some recent episodes:
From Head Count to Heart Count: Loyalty by Design with Joey Coleman. In this episode, I sit down with Joey Coleman, founder and Chief Experience Composer of Design Symphony and bestselling author of Never Lose a Customer Again, and Never Lose an Employee Again, to uncover why most organizations lose up to 70% of customers and employees in the first 100 days.
Humanizing a Legacy Brand: From LEGOs to Insurance with Conny Kalcher. In this episode of Humanity at Scale, host Bruce Temkin is joined by Conny Kalcher, Group Chief Customer Officer at Zurich Insurance Company, to discuss reimagining customer experience in large organizations. They explore moving beyond transactions to build meaningful, empathetic customer relationships.
Designing The Future: How to Be a Good Ancestor with Lisa Kay Solomon. In this episode, I’m joined by Lisa Kay Solomon, Designer in Residence at Stanford’s d.school, for a powerful conversation about leading with imagination in an era of disruption. We explore how leaders can actively shape the future by cultivating foresight, ethical decision-making, and human-centered design.
The Ethics of Empowerment: How AI Can Make Us Stronger with Vivienne Ming. In this episode of Humanity at Scale, I sit down with Dr. Vivienne Ming, a visionary neuroscientist and AI pioneer, to explore how technology can elevate, not replace, human potential. Sharing her inspiring journey from homelessness to innovation leadership, Ming unpacks how purpose, ethical design, and a deep understanding of human complexity should shape AI development.
Empathy, AI, and the New Rules of the Human Workplace with Erica Keswin. In this episode, I sit down with WSJ bestselling author, human workplace expert, and keynote speaker, Erica Keswin and we explore the future of human-centric leadership in a tech-driven world.
The podcast is available on Apple Podcasts, Spotify, and YouTube.
Humanity at Scale is a movement to inspire and empower leaders to create humanity-centric organizations