The problem
Four hours. That’s how long it took to answer one campaign question.
Our regional marketing team needed insights to make decisions. Not eventually. Now. But the data was scattered across four different systems. Client information lived in the CRM. Campaign performance was in Tableau. Historical data was in JIRA. Operations details were somewhere else entirely.
Every time someone asked “how is this campaign performing?” I had to manually hunt through each system, piece together the information, and write it up. By the time I had the answer, the decision window had closed. We were already committed to the next thing.
The real cost wasn’t the four hours. It was the reactive decision-making. We were always one step behind, analyzing what already happened instead of shaping what comes next.
The insight
I realized something: the problem wasn’t that we lacked data. We had too much data, fragmented across too many places.
What if instead of making someone manually collect and synthesize that data, I built an agent to do it? An agent that could search multiple systems simultaneously, consolidate the information, and present it in a format we could actually use.
More importantly: what if I structured it not as a single tool, but as the first step in a larger workflow? Scout collects the data. Strategist analyzes it. Copywriter turns it into recommendations. Human reviews and decides.
Each step is an agent. Each step is automatable. The only human requirement is judgment, not data processing.
The approach
I built an AI agent on Toqan (no-code platform) that acts as the Scout.
Its job: search across our fragmented data sources and consolidate everything into one searchable interface. Ask it about any campaign, it finds what you need in seconds.
From there, I added the Strategist agent. It takes the Scout’s findings and analyzes them. What patterns emerge? What’s working? What’s not? Where should we focus?
Then the Copywriter agent. It takes the Strategist’s analysis and turns it into actual written recommendations. Not raw data, but narrative. “Here’s what’s happening, here’s what it means, here’s what we should do.”
Finally, the human review step. Someone with judgment reads the output, validates it makes sense, and decides whether to act on it.
The whole pipeline runs in parallel. Agents handle the work. Humans handle the decision.
Why this matters
The metrics tell the story:
Time:
- Before: 4 hours per campaign analysis
- Now: 3 minutes per campaign analysis
- Result: 98% time reduction per analysis
Decision quality:
- Before: Reactive (we analyzed what already happened)
- Now: Proactive (we ask questions and make decisions while it matters)
But the bigger shift is psychological. When you can answer campaign questions in 3 minutes instead of 4 hours, you ask more questions. You test more hypotheses. You catch problems earlier.
That’s not just faster. That’s fundamentally different decision-making.
The principle
Most people think about AI as task automation. Make one thing faster.
But agentic workflows are about restructuring how work gets done. You’re not optimizing a step. You’re removing the bottleneck entirely.
The agent does what it’s good at: processing data, finding patterns, generating output. Humans do what they’re good at: judgment, strategy, ownership.
The workflow only works when both sides are clear about their role.
What I learned
Building this taught me that I was pioneering something most marketing teams hadn’t even considered. Agentic workflows weren’t a buzzword then. They were just a solution to a real problem.
The second lesson: the best workflows aren’t faster versions of old workflows. They’re different workflows entirely.
When you move from 4 hours to 3 minutes, you don’t optimize the old process. You change what becomes possible. You shift from reactive to proactive. You go from “why did that fail?” to “what should we test next?”
The question
What workflows in your team are bottlenecked by manual data processing?
What if you could restructure that workflow so agents handled the work while humans made the decisions?
That’s where agentic thinking becomes valuable.
Ready to explore agentic workflows for your team?
If you’re thinking about how to restructure your workflows around AI agents, or you need help building custom systems that move your team from reactive to proactive decision-making, let’s talk.