The Recipe for Agentic Browsers: Trigger + Action
If you’ve ever told an AI assistant to “take over” or “handle this” and then watched it stall, ask clarifying questions, or do… nothing—you’re not doing anything wrong.
The problem isn’t the AI.
It’s that something essential is missing.
Every agentic system—AI or otherwise—runs on the same simple recipe:
Trigger + Action
Once you see it, you can’t unsee it.
This Isn’t an AI Thing. It’s an Automation Thing.
The recipe for agentic browsers, AI assistants, and automations of any kind always has two parts:
- A trigger (when something happens)
- An action (what should happen next)
That’s it. No magic. No mystery. And none of this is new.
I’ve spent years building, operating, and scaling systems that work exactly this way—long before conversational AI entered the picture.
How This Shows Up in Real Systems
At Troops: Event-Driven Intelligence Between Systems of Record and Engagement
At Troops, I worked as a senior CSM for a revenue communications platform that sat between systems of record—primarily Salesforce, alongside tools like Gainsight, Dynamics, Zendesk, and Outreach—and systems of engagement like Slack and Microsoft Teams.
In practice, that meant helping go-to-market teams stop living inside CRM UIs and instead manage deals, renewals, and escalations directly from chat—without breaking data integrity in the CRM.
Troops worked by continuously monitoring CRM objects and fields—opportunities, accounts, renewals, tickets—and firing event-based signals when something meaningful changed: a deal slipped, a stage updated, a renewal went at risk, or a ticket escalated.
Those events were delivered into the right Slack or Teams channels or DMs, paired with next-best-action buttons that let reps and CSMs update fields, log activity, or kick off workflows inline. Every action flowed bi-directionally back to Salesforce and other systems of record with the correct validations and permissions intact.
The pattern was always the same:
- Trigger: A meaningful change occurs in a system of record
- Action: The right people are prompted—in chat—to take the next step
Without a clear trigger, nothing surfaced. Without a clear action, the signal went unused.
My role as a CSM was to help customers design these workflows around their actual revenue processes—pipeline reviews, renewal cadences, escalation paths—so signals aligned with KPIs, improved data quality, and drove real outcomes like faster deal cycles and better forecasting.
Different product. Same recipe.
At Slack: Workflow Builder & Enterprise Migrations
I saw this same Trigger + Action pattern everywhere at Slack—most clearly in Workflow Builder and in large-scale Enterprise Grid migrations.
Workflow Builder isn’t about novelty automations; it’s about operational clarity. Each workflow is explicitly defined by:
- Trigger: A specific event occurs (a form is submitted, a message is posted, a reaction is added, a schedule is reached)
- Action: A concrete operational step follows (create a ticket, update a system, notify stakeholders, route work, enforce a process)
For example:
- A form is submitted → create a Jira ticket and notify the owning team
- A message appears in a support channel → log the issue and alert on-call
- A scheduled time is reached → post a checklist or reminder to drive consistency
No trigger, no workflow. No action, no outcome.
In Enterprise Grid migrations, the same Trigger + Action logic applied—just at significantly higher stakes.
Slack Enterprise Grid migrations aren’t single-step events; they’re multi-phase programs governed by clear system signals. Each phase produces explicit state changes—pre-migration readiness, data export completion, workspace moves, validation checkpoints—that act as triggers inside the migration system.
Those triggers then drive enforced actions, not suggestions:
- Stakeholders are notified at the right moments
- Escalation paths activate when blockers appear
- Migration specialists engage or disengage based on readiness signals
- Migration status reflects real progress, not assumptions
Nothing advanced unless prerequisite conditions were met. Nothing stalled without visibility.
The system worked because migration progress was treated as event-driven execution, not manual coordination. The platform didn’t infer intent. It reacted to explicit triggers with predefined actions—at scale, across hundreds of enterprise workspaces.
Same recipe. Higher consequences.
A Non-Technical Example: Ordering Food
This pattern isn’t unique to software.
Imagine walking into a restaurant and saying:
❌ “Handle my dinner.”
The server would have to stop and ask questions. What kind of food? For how many people? Any preferences? Any restrictions?
Now compare that to:
✅ “I’d like the grilled salmon, no onions, with a side salad.”
Same trigger—you placed an order. But only the second one includes a clear action.
Nothing about the kitchen changed. The difference was clarity.
Agentic systems work the same way:
- If you only tell them when to act, they stall.
- If you also tell them what success looks like, they execute.
That’s not intelligence. That’s instructions.
The browser isn’t failing—it’s waiting for the rest of the order.
Why This Matters for Agentic Browsers
Agentic browsers—tools that can navigate, create, and act on your behalf—work the exact same way.
This is where people get tripped up.
❌ “Take over my browser.”
✅ “Take over my browser and create a new note titled ‘Project Ideas.’”
The first command has a trigger, but no clear action. The second has both—so the system can execute.
The pattern is always:
Trigger (your command) + Action (the outcome you want)
If you only give one, the system doesn’t have enough information to move forward.
How to Apply This (Anywhere)
When you prompt any agentic system, run this quick mental checklist:
- Trigger: When should this run?
- Action: What outcome do I actually want?
If either of those feels fuzzy, the system will be too.
This applies to AI, workflows, automations—and honestly, most operational systems in general.
The Real Aha
This framework was so internalized for me—from years in customer success, enterprise migrations, and release management—that I didn’t even realize I wasn’t applying it to conversational AI.
It only clicked after a frustrating interaction with an agentic browser.
That’s the funny thing about deep expertise: sometimes it’s so baked in that we forget to name it—even for ourselves.
And that’s why I believe in learning in public.
It’s how invisible frameworks become visible—and reusable.