11 Jul 2025 5 min read marketing technology

Agentic AI in Martech: The Handoff

Part 6: Practical experiments for teams ready to delegate

After five posts exploring the theoretical challenges of agentic AI, scale, complexity, conflicts, roles, and trust, I want to end this series with something more practical. If you're convinced that agentic systems represent the future of Martech but aren't sure where to start experimenting, what's the actual first step?

Based on the patterns I've observed and my own experiments, I think the answer lies in designing deliberate handoff protocols. Not grand AI strategies or comprehensive automation plans, but careful experiments in delegation that help you learn how to work with autonomous systems.

Starting with low-stakes decisions

The teams that seem most successful with agentic AI start with decisions that matter but don't catastrophically damage anything if they go wrong. Email send time optimization is a classic example. If the AI chooses poorly, you might see slightly lower open rates, but you're not going to lose customers or damage your brand.

I've been experimenting with this linear approach in my own work. Instead of jumping straight to complex personalization or customer journey automation, I started with simple delegation boundaries: let the AI optimize the timing of my newsletter sends based on subscriber engagement patterns, but keep human control over content and audience selection.

The results have been mixed, which is exactly what you'd expect. Some weeks the AI picks better send times than I would have chosen. Some weeks it makes choices that seem obviously wrong in hindsight. But the key insight is that I'm learning to distinguish between AI decisions I trust and ones that trigger my skepticism.

Building confidence through reversibility

One pattern that's worked well is focusing first on highly reversible decisions. If the AI chooses the wrong email send time, I can adjust the timing for next week. If it picks the wrong subject line variant in an A/B test, I can update the approach for the next campaign.

This reversibility creates a safe space for both human and AI learning. The AI gets to experiment with different approaches and learn from the results. I get to observe its decision-making patterns and calibrate my trust accordingly.

Some teams are taking this further by creating explicit "learning periods" where agentic systems are given autonomy to experiment, but with the understanding that humans will review the results and potentially override the AI's learning if it heads in problematic directions.

Boundary definitions

Before handing any decision to an agentic system, I've found it useful to explicitly define the boundaries of acceptable behavior. This is about clarifying your own decision-making philosophy upfront.

For example, when experimenting with AI-driven email frequency optimization, I had to define what "too much" communication actually means. Is it more than five emails per week? More than three emails in a single day? Does it depend on the customer's engagement level? Their subscription preferences? Their purchase history?

These questions force you to articulate business logic that was previously implicit. The process of defining boundaries for AI delegation often reveals assumptions you didn't know you were making about customer preferences, brand voice, or business priorities. Luckily enough we can do that in our new preferred programming language… English.

Monitor intent

Traditional marketing analytics focus on outcomes, open rates, click rates, and conversion rates. But when working with agentic systems, I've learned to monitor intent as well. What patterns is the AI detecting? What correlations is it acting on? What strategies is it developing?

This requires different dashboards than most teams are used to building. Remember those ‘reasoning dashboards/monitors’ I mentioned earlier in the series? Instead of just showing what happened, these interfaces need to show what the AI thinks is happening and why it's making certain choices.

Some teams are experimenting with these "AI reasoning logs" that capture not just the decisions the system made, but the patterns and correlations that influenced those decisions. This creates a kind of audit trail for AI thinking that helps humans understand whether the system is learning useful patterns or getting distracted by spurious correlations.

When and how…

One of the most important aspects of any handoff protocol is defining when and how to escalate decisions back to humans. This needs to be more sophisticated than simple rule-based triggers, because the most interesting agentic decisions often involve novel situations that wouldn't trigger predefined rules.

I'm experimenting with confidence-based escalation, where the AI flags decisions it's uncertain about for human review. Because at the end of the day, most of this is about propensity. But calibrating these confidence thresholds is tricky. Set them too high, and you get escalations for every minor decision; set them too low, and you miss the cases where human judgment would actually be valuable.

Some teams are building escalation triggers around customer value or business impact. Decisions involving high-lifetime-value customers get human review. Choices that significantly deviate from historical patterns get flagged. Actions that could affect brand perception require approval.

Be surprised

Perhaps the most important skill I've developed while working with agentic systems is learning to distinguish between surprising decisions that indicate good AI learning and surprising decisions that indicate AI confusion.

Good surprises often involve the AI discovering patterns you hadn't noticed. Maybe it realizes that customers who engage with content on mobile devices respond better to shorter subject lines, or that certain customer segments prefer educational content over promotional messages.

Bad surprises usually involve the AI acting on spurious correlations or optimizing for metrics that don't actually matter. Maybe it starts sending emails at 3 AM because it detected a tiny uptick in open rates during overnight hours, ignoring the fact that most people don't check email at that time.

Building this intuition takes time and requires accepting that you'll sometimes get it wrong. But I've found that teams develop better judgment about AI behavior when they're actively experimenting with delegation rather than just theorizing about it.

Feedback loop

Successful handoff protocols require designing good feedback loops between human oversight and AI learning. When you override an AI decision, the system needs to understand why. When the AI discovers something useful, you need mechanisms to reinforce that learning.

This is harder than it sounds because human feedback often involves context that's difficult to encode in ways AI systems can use. You might override an AI's email timing decision because you know there's a company holiday that week, but teaching the AI to consider company holidays in future decisions requires updating its training data or decision framework.

Some teams are experimenting with structured feedback systems where humans provide not just approval or disapproval, but reasoning that helps the AI learn better patterns. Instead of just saying "don't send emails on Friday afternoons," you might specify "avoid Friday afternoons during summer months when engagement typically drops."

Give it a go yourself

If you're ready to start experimenting with agentic AI delegation, I recommend beginning with this framework:

Choose one low-stakes, reversible decision that you make regularly.
Define clear boundaries for acceptable AI behavior.
Implement monitoring that shows both outcomes and AI reasoning.
Design escalation triggers that flag decisions for human review.
Create feedback mechanisms that help the AI learn from your overrides.

Start small, learn actively, and expand gradually as you build confidence in both the AI's capabilities and your own ability to work effectively with autonomous systems.

The future of marketing operations will likely involve extensive collaboration between humans and agentic AI. The teams that start experimenting with this collaboration now will have significant advantages over those who wait for the technology to become more predictable.

Because agentic AI will probably never be fully predictable. But it might become productively unpredictable in ways that create new possibilities for marketing effectiveness.

And learning to work with productive unpredictability feels like a valuable skill to develop.

What's your first agentic AI experiment going to be? Are there specific delegation boundaries or escalation triggers you're considering? I'm curious about what others are discovering as they start handing decisions over to autonomous systems.

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