I’ve spent the last week deep inside our Agentforce sandbox, and I need to be honest about something that Salesforce is not talking about enough.
Everyone is obsessing over the “Agent” part with the cool autonomous sending, the natural-language magic, the shiny new UI. But almost nobody is talking about the context window!!!!
The Brutal Reality
If you point Agentforce at a standard Salesforce instance, you’re basically building a very expensive, very fast spam cannon.
An AI agent is only as smart as the data you feed it. And let’s be real: Your average Salesforce instance is a graveyard full of “Test Test Ahmed Cheetos” leads, stale stuff from 2022, and scary inconsistent fields that nobody has touched in years.
This is exactly why I’ve been obsessed with the Clay <> Salesforce integration lately. It’s not just “enrichment.” It’s the pre-processing logic layer your agents actually need to function.
How We Are Rebuilding Our Clients' GTM Architecture (And Why It Works!)
This month, we completely rebuilt the GTM data architecture for one of our clients, and the difference has been almost scary!!!
Instead of treating Clay like a list-builder, we configured it as a live context engine:
- Clay runs a waterfall across 10+ providers
- It scrapes the company’s last blog posts
- It analyzes the prospect’s recent LinkedIn activity
- It checks for buying and intent signals like hiring, layoffs, or tech stack changes
Only after all that research is done do we push the record into Salesforce.
But here’s the key part:
We pipe all that unstructured research into custom fields in SFDC.
So when Agentforce wakes up to work a lead, it doesn’t just see:
“Hamda Helal — Sales Director.”
It sees:
“Hamada Helal — recently posted about reducing CAC, company just installed HubSpot, hiring 5 SDRs.”
Because Agentforce has native access to those Salesforce objects, the emails it drafts aren’t generic AI slop. They reference the actual pain points surfaced by Clay. It reads like a senior AE who spent 20 minutes researching, except it happens instantly, and at scale!
The Results
- Response rates tripled because the relevance is impossible to ignore.
- Data costs dropped since they stopped paying for leads lists.
- Relevance → Revenue because the CRM is no longer hollow.
If you’re a GTM engineer: stop obsessing over prompt tuning and start looking at your data pipe. The best AI model in the world can’t fix broken inputs. You need the infrastructure before you turn on the robot.
If you wanna try something like this, connect with us and we will sort you out!