The Four Verbs of Agentic Marketing

How Sense, Decide, Act and Learn Are Rewiring Modern Marketing

Most AI conversations in marketing are stuck on features. But the real transformation isn’t about the model. It’s about the operating model.

Agentic systems are changing the game by introducing a new rhythm — a continuous loop built around four essential verbs: Sense, Decide, Act and Learn.

This isn’t a framework for theory. It’s a playbook for action.

From personalisation at scale to fully autonomous media buying, the SDA(L) loop underpins how forward-thinking marketers are shifting from episodic execution to continuous optimisation. It’s fast, adaptive, and commercial by design.

Let’s break it down.

Sense

Data Isn’t Useful Until It’s Active

Marketers are drowning in behavioural data - customer data, clickstreams, cart drops, loyalty events, search queries and chat transcripts. But most of it sits idle. Agentic systems don’t need more data; they need better sensing.

Sensing is about detecting signal in real time: the right data, structured and consented, at the right moment.

What Good Sensing Looks Like

• Event-level interactions: Browsing, cart, app sessions

• Platform behaviours: Session recency, intent scoring

• Enrichment signals: Inventory, price shifts, loyalty tiers

• Privacy-aware architecture: Consent-respecting and compliant

Example:
24S (LVMH) feeds live stock signals into its CRM, driving timely nudges that lift conversions. Ulta Beauty reacts to real-time basket composition to surface tailored recommendations. No manual build. Just better sensing.

Why It Matters:
If your agents don’t sense a change, they can’t respond. This is the foundation layer — and too many brands are still treating it as a data plumbing exercise.

Decide

Strategy, Codified

After sensing comes choice. But here’s where most brands fall short: they automate action without ever defining decisioning.

Agentic systems don’t guess — they follow encoded policy. Your brand’s rules, preferences, and constraints become the logic layer.

What Smart Decisioning Looks Like

• Next-best-actions: For this user, now, under these conditions

• Guardrails: Frequency limits, margin constraints, eligibility filters

• Trade-offs: Short-term conversion vs long-term value

• Escalation logic: When to defer to human review

Example:
Klarna’s AI agent determines, in real time, whether to approve returns or escalate based on a mix of customer signals and business policy. In media, performance agents dynamically rebalance spend across SKUs as stock and performance shift.

Why It Matters:
This is how you maintain brand integrity while scaling automation. Decisioning isn’t tech. It’s strategy, made executable.

Act

The Execution Bottleneck Is Over

Most marketing teams don’t fail on ideas. They fail on execution. Too many handoffs. Too much lag.

Agentic systems remove that bottleneck. Decisions become actions. Instantly.

What Autonomous Action Looks Like

• Triggering emails, push or SMS

• Updating CRM paths or bid modifiers

• Generating creative assets with templates

• Creating tickets or resolving requests in-service flows

• Pushing new content into paid campaigns

Example:
24S uses low-stock signals to trigger journey updates automatically — no human intervention. Klarna now spins up thousands of creative assets using AI, slashing production cycles. Expedia lets customers rebook itineraries through autonomous flows.

Why It Matters:
Act is where value lands. It’s the moment your strategy becomes reality — without the middlemen.

Learn

From Attribution to Adaptation

Traditional marketing teams learn quarterly. Agentic systems learn continuously.

Every action creates an outcome. Every outcome trains the system.

What Continuous Learning Looks Like

• Capturing feedback (clicked, bounced, unsubscribed, returned)

• Using models like multi-armed bandits or reinforcement learning

• Reallocating budgets or rewriting decision logic in real time

• Optimising creative, journeys, and bidding with every loop

Example:
GlassesUSA refines real-time product recommendations based on clickstream learning. Media agents dial up spend on high-performing SKUs within hours — not weeks.

Why It Matters:
Learning isn’t a phase. It’s the flywheel. The more the system runs, the smarter it gets.

Closing Thought: Replace Lag With Loop

The Sense–Decide–Act–Learn loop is more than an AI playbook. It’s the new operating model for performance marketing.

It lets teams do more with less. It turns data into action. And it shifts the role of marketing from manual execution to system supervision.

Agentic marketing doesn’t replace humans. It replaces lag — the time between intent and impact. The result is a system that scales faster, learns faster, and performs better than legacy workflows ever could.