For the past year, the industry has been virtually drunk on vibe coding.
And why not? All you do is describe what you want and the AI writes the code. The Flow appears. The demo works. Everyone nods knowingly. Ah yes, the future has arrived!
It feels like magic because the generation part of it — the hardest part for millennial, I say, as a writer — suddenly feels easy.
You don’t need to memorize syntax. You don’t need to remember every API name. You don’t need to know whether the picklist value is technically “Healthcare” or “Healthcare & Pharma.” You just describe the outcome. The machine fills in the blanks.
For their part, Salesforce calls it “vibes building.” Developers use Cursor or Windsurf and prompt their way through Apex. Configuration, once laborious, now materializes in seconds.
And fine, yes, this is real progress. But it’s only Stage One.
And we’re already starting to see the cracks.
Recently, a research paper on multi-turn LLM conversations made a sobering point: models don’t usually fail because they’re dumb. They actually fail because intent drifts. As humans clarify their requests in fragments — adding constraints, revising assumptions, revealing edge cases — the model anchors to an earlier interpretation and compounds the error.
And yet, the conversation continues and alignment degrades. Bigger models don’t reliably fix it. Memory alone doesn’t fix it. The problem isn’t intelligence. It’s grounding. It's context.
That observation has nothing to do with Salesforce — not specifically, anyway.
It has everything to do with vibe coding.
Because enterprise system changes are almost never a single, clean instruction. They are iterative. Contextual. Layered. “Create a Flow” quickly becomes “Actually, use the existing field… but only when it’s blank… and make sure it doesn’t conflict with the automation the other team built… and by the way, our picklist values are custom.”
In other words: intent drifts.
Generation keeps up; reality does not.
Generation Is Solved, The Engineering Part Is Not
Modern LLMs are remarkably good at producing syntactically correct Salesforce metadata and code (presumably that's because they've been trained on a lot of it). Ask for a record-triggered Flow and you’ll get a Decision element, proper branching, assignments, and the right trigger context. Ask for an Apex trigger and you’ll receive something that looks entirely plausible.
On paper, this is both rad and extraordinary. But engineering was never just about writing something that looks right. Engineering has always been about constraints:
- What already exists?
- What depends on this?
- What will break if I change it?
- What doesn’t match my assumptions?
Those questions are brutally specific to a live system.
And that’s where all these "Stage One" tools struggle.
Vibe coding generates against a statistically average Salesforce org. It assumes fields exist. It assumes picklist values match your wording. It assumes no conflicting automation is already running. It assumes your environment looks like documentation examples.
But oh my sweet summer child, while that assumption holds in small, clean demos, in real enterprise orgs — with thousands of fields, layered Flows, legacy validation rules, half-retired processes, and overlapping automation — it really does not.
The failure mode isn't as "catastrophic" as it is something subtler: deployment errors, edge-case bugs, execution-order conflicts, permission oversights. The AI didn’t misunderstand Salesforce. It misunderstood your org.
Why More Intelligence Doesn’t Solve a Context Problem
It’s tempting to believe this is just a scaling issue. That the next model release will close the gap. That better reasoning will compensate for missing specificity.
But the research tells us otherwise. When models operate without fully grounded intent, they fall back to priors. They assume the most statistically common pattern. They interpret ambiguity in the most likely way.
If your org’s Industry picklist uses “Healthcare & Pharma” and you prompt “Healthcare,” like it did in this Salesforce Ben experiment — the model is behaving rationally given incomplete context.
No amount of general intelligence can infer a value that only exists inside your specific Salesforce instance.
It’s a grounding failure, after all. And grounding requires something different than generation. It require an evaluative brain.
Flat Files Are Not Understanding
Some teams are trying to work there way around it already, mostly by attempting to solve it by syncing metadata into SFDX projects and pointing AI coding tools at the XML files. That absolutely does improve accuracy. The model can see field API names. It can reference objects more reliably.
But metadata files are deployment artifacts. They were not designed to function as a relational knowledge system.
Ask an AI: “What touches the Status field?”
It will grep the repo. You’ll get hundreds of hits. Because “Status” appears in field names, picklist values, Flow conditions, validation rules, Apex code, and even comments across dozens of objects. The model sees text occurrences. It does not understand relationships. It cannot distinguish between Case.Status and a custom Status__c field elsewhere. It cannot tell you which automation reads the field versus writes to it. It cannot model execution order.
The files contain data.
They do not contain computed relationships.
They do not contain system understanding and yes, engineering requires understanding. When you build a bridge, you don't build a generic one. You build a bridge that understands the environment in which it needs to be built.
Stage Two: Agentic Engineering
This is where we enter Stage Two. And it's about time.
If Stage One was about AI generating artifacts, Stage Two is about AI operating inside real system constraints.
We call it agentic engineering.
Agentic engineering begins from a different premise. Instead of asking, “Can the AI produce the right metadata?” it asks, “Can the AI produce the right metadata for this exact org?”
That requires three things to happen before deployment:
First, the system must understand the live org — not from documentation, not from examples, but from querying the actual schema, existing automation, field types, picklist values, and dependencies.
Second, every generated artifact must be validated deterministically against that live state. Do references resolve? Do field types match? Do picklist values exist? Are there unsatisfied dependencies? These are not probabilistic judgments. They are binary checks.
Third, ambiguity must be surfaced, not guessed. When a prompt collides with multiple possible interpretations, the system should ask, not assume.
In other words: behave like a careful engineer (not, say, a prolific intern).
Sure, the difference there might feel subtle in a demo, but it feels enormous in prod.
From Generation to Verification
In a pure vibe workflow, the sequence looks like this:
Generate to Attempt Deployment to Debug to Try Again.
In an agentic engineering workflow, it inverts:
Understand Org to Identify Blockers to Clarify Ambiguities to Generate to Verify to Deploy Atomically.
That inversion is the entire category shift. It transforms AI from a drafting assistant into a system participant.
Where Sweep Fits In To This Framework
Sweep was built on a simple thesis: generation is now the easy part. Org-aware generation plus deterministic verification is the hard part. That’s why Sweep Build Mode doesn’t start by writing metadata.
It starts by querying your live Salesforce org.
Before generating a Flow, it retrieves exact picklist values, confirms object structure, inspects active automation, detects missing fields, and maps dependencies. If something in your prompt doesn’t match reality, it surfaces it immediately. If a field doesn’t exist, it proposes creating it. If a picklist value is slightly different, it flags the mismatch. If another Flow already runs in that context, it warns you before execution-order conflicts emerge.
Only after grounding in the org does generation occur.
And before anything touches production, Sweep runs deterministic verification checks — programmatic validation against the live org to confirm every reference resolves and every dependency is satisfied.
Deployment is atomic. If something fails, nothing half-deploys.
For developers working in Cursor, Windsurf, or VS Code, the Sweep MCP Server extends that same intelligence layer directly into the IDE. Your AI copilot doesn’t just know generic Salesforce patterns — it knows your schema, your fields, your automation graph.
Vibes Were Necessary: They Were Also Insufficient
It's true: the industry needed Stage One. We needed to trust that natural language could build a real configuration. We needed the barrier to drop. We needed generation to become cheap.
But as AI moves from drafting emails to modifying production systems that run revenue and service operations, the stakes rise.
A blog post can tolerate approximation (I mean, within reason).
A CRM automation cannot.
The research on intent drift makes one thing clear: without grounding, alignment degrades over time. Enterprise systems amplify that risk. The more layers of automation you add, the more expensive a misinterpretation becomes.
That’s why Stage Two has become less optional.
Ready for Stage Two?
If you’re experimenting with vibe-coding today, you’re participating in the right wave. But you're also becoming a laggard if you’re deploying AI-generated changes into real enterprise Salesforce orgs with nothing else.
You need live org awareness.
You need deterministic verification.
You need atomic deployment.
You need agentic engineering.
Sweep Build Mode turns natural language into verified, deployable Salesforce configuration — grounded in your org, not an imagined one.
See how Sweep moves you from vibes to engineering.
Book a demo and step into Stage Two.