Patterns for Letting the AI Do the Work

One of the easiest mistakes to make when designing AI-driven systems is to give people three versions of the same thing.

First you tell them the “insight.” Then you tell them the “recommendation.” And then you build a “workflow” with the same steps you just described. It feels thorough, but in practice it’s just noisy. Users start asking: wait—do I act on the insight, approve the recommendation, or run the workflow?

The better pattern is simple: collapse redundancy, and let the AI act first. Insight should trail action, not the other way around.

Here are some patterns that help.

1. Agent/Operator

Treat the AI like it’s on the team. Not a commentator, not a narrator—an operator.

Think of GitHub Copilot: when it offers an “autofix,” it doesn’t say “there’s a problem, here’s some advice.” It just writes the fix. You can approve or edit. The AI did the heavy lift.

2. One Expression per Function

If you say something once, it should live in exactly one place.

Google Maps doesn’t show “traffic” in one card, “recommend reroute” in another, and “new navigation steps” in a third. It just updates your route.

Same idea here: don’t describe a Lis Pendens in Insight, repeat it in Actions, and list it again in Workflow. Show it once, connected to the action it triggered.

3. Preview, Then Commit

People like to see what’s going to happen before the AI runs.

That’s the whole idea behind Gmail’s Smart Replies or your Deploy Agent drawer. The system generates a plan. You glance at it. And then you either approve all, or make a tweak. Default to approval, not micromanagement.

4. Contextual Automation

Context belongs inside the action, not floating around it.

Instead of:

• “Lis Pendens filed”
• “Recommendation: outreach”
• “Workflow: outreach scheduled”

Collapse it to: “Lis Pendens filed → Outreach scheduled.” One line, complete.

5. Feedback Loops

Once an AI Agent is “Active,” it needs to talk back.

Don’t just mark the plan as “running.” Feed small updates: “Agent re-scored risk to 85 after lien posted.” The user sees it’s alive, monitoring, and adjusting.

6. Default to Automate

Instead of waiting for the user to greenlight every micro-task, assume yes.

Deploy Agent should generate a plan and run it. If someone wants to dial it back, they can. That’s a more honest use of the word “agent.”

7. Narrative Collapse

Narratives are good, but spread across too many places they just become clutter.

Rather than Insights here, Workflow there, Entities somewhere else—bundle it. Call it the Agent Narrative. Make it clear: this is the AI explaining why it’s doing what it’s doing, in one voice, not six widgets.

Planning Ahead

• Confidence thresholds. Let users set: high confidence → auto-execute, medium → ask me, low → just show insight.
• Sub-agents. In time, you may want “Claimant Analyzer” or “Tax Redemption Tracker” agents reporting up to a parent Agent.
• Portfolio intelligence. The real prize: letting an Agent notice patterns across deals, not just one.

The Takeaway

If you build for redundancy, you end up with an AI that sounds smart but doesn’t feel useful. If you build for automation, you get the opposite: a system that moves first, explains briefly, and earns trust.

That’s the shift: from AI as commentator, to AI as operator. And the patterns above are the handholds that make the climb possible.