The Hidden Cost of AI Hand-Offs: Why Agentic Efficiency Can Erode Value If You’re Not Watching the Gaps
When multiple people collaborate to complete a process, whether it’s a sales cycle, construction project, or client deliverable, we know instinctively where the trouble lives. It’s in the handoffs. That’s where things fall through the cracks, deadlines slip, and assumptions multiply. It’s where accountability gets fuzzy and efficiency quietly drains away.
Surprisingly, the same principle applies when AI agents, not people, are executing the process.
Agentic AI, the new generation of autonomous digital workers, promises to revolutionize productivity by letting you assign tasks and let the system “figure out the rest.” But as a recent article by Shumaker, Loop & Kendrick LLP (“Chatty Chatbots: Why AI Agents Are the Silent Threat to Your Company’s IP”) points out, these agents can also introduce invisible risks that look eerily similar to human failure points, only faster, larger, and harder to detect.
When Agents Pass the Baton
In a well-designed workflow, people document what they’ve done, clarify what’s next, and verify that the next person understands before they hand off.
AI agents, by contrast, don’t pause for understanding, they share context.
That sounds harmless enough until you realize what “sharing context” often means: dumping entire documents, conversations, or data sets into another agent’s workspace “just in case it’s needed.”
Shumaker calls this a context dump. It’s the digital equivalent of handing your teammate the entire filing cabinet rather than the one folder they actually need. It may feel like collaboration, but it’s actually an efficiency leak and a risk amplifier.
The article warns that these over-inclusive handoffs can expose trade secrets, confidential negotiations, or unfiled intellectual property to systems or vendors that were never intended to see them.
Just as a misinformed employee can inadvertently forward a sensitive spreadsheet, a loosely configured AI handoff can replicate that mistake thousands of times, at machine speed.
Why the Human Analogy Matters
Most organizations already have a muscle memory for managing human handoffs.
We build process maps, RACI charts, and approval workflows. We assign ownership, sign-offs, and checks. We audit what went wrong and tighten the process next time.
Yet when we introduce AI agents into the mix, we tend to forget that they too are part of the chain, and that the same principles of discipline and accountability still apply.
The myth of “autonomous” AI suggests that once an agent is trained, it operates safely and efficiently on its own. But autonomy without governance is just chaos with an API.
Like people, agents need clearly defined boundaries: what they can access, what they can share, and when their authority expires. Without these guardrails, even well-intentioned agents can create new inefficiencies, wasting compute cycles, retrieving unnecessary data, or corrupting deliverables with irrelevant context.
The result? A modern version of the same old problem: poorly managed handoffs costing time, money, and trust.
Bringing Operational Discipline to Digital Workflows
The fix isn’t to avoid agentic AI, it’s to manage it with the same rigor we apply to people.
Here are five practical principles drawn from both Shumaker’s legal insights and decades of operational best practice:
Define the lane before assigning the task.
Each agent should know precisely what data it can touch and for how long. “Need to know” isn’t just a security rule, it’s an efficiency rule.Segment memory.
If agents store information for later use, separate that memory by sensitivity: restricted, confidential, general. Don’t let sensitive data leak from one project or agent to another.Audit the handoffs.
In human teams, we hold project reviews. In AI workflows, run periodic trace audits to see what information moves where. If you find duplication or irrelevant sharing, you’ve found your first efficiency win.Short-lived credentials.
Don’t give agents master keys. Grant temporary access, then revoke it automatically. In the human world, we call that “offboarding.” The same logic applies here.Reward refusal.
Build systems that celebrate when an agent refuses to act outside its scope. That’s a sign of governance working, not failure.
The New Face of Diligence
For CEOs and boards, this isn’t just a technology conversation , it’s a governance conversation.
AI agents are now handling sensitive company knowledge, pricing models, client data, and strategic plans. They can be as powerful as a new department, or as dangerous as an untrained one.
The discipline you bring to managing human handoffs must now extend to digital ones.
Process diligence, data labeling, and permission control aren’t IT tasks; they’re leadership responsibilities.
When done well, the payoff is enormous: faster cycle times, lower error rates, and scalable decision support. But when neglected, the hidden costs accumulate until your supposed productivity engine becomes a silent drain on intellectual capital, or worse, a source of exposure.
Bridging the Human and the Machine
At Pathfinder Group, we often remind clients that systems don’t create discipline; people do.
Agentic AI will transform how work gets done, but it won’t change the truth that value depends on the quality of the handoff. Whether it’s from one person to another, or one agent to the next, the same rule applies:
If you don’t manage the handoff, the handoff will manage you.
