The Rise of Personal Apps on Salesforce

If you work with Salesforce, you might have wondered how the rapid development of AI coding abilities will affect the future of the Salesforce ecosystem. Will we all vibe-code our own CRMs? Will agents need CRMs at all? Will Agentforce ever take off?

As an AI-pilled software founder and Salesforce Certified Technical Architect with a decade of experience in the Salesforce ecosystem, I think we’re in luck. Let me explain.

The great split

We are all striving to put AI coding into our workflows. What started as fancy autocomplete has now transitioned through the rapid takeoff of agentic coding harnesses into loops, graphs, and dark software factories. But a split is emerging, and the two sides of it are increasingly talking about entirely different things.

On one hand, we have enterprise systems and complex applications. Long-lived, critical codebases with many moving parts and people involved. The other side is the now-possible domain of ‘vibe-coded’ or personal software, software that was never previously commercially feasible at scale. AI coding capabilities are undoubtedly improving rapidly for both, but the risks involved and how value can be captured are very different across both.

Companies applying agentic coding at scale are seeing massive output but are also discovering new bottlenecks. Senior engineers are overwhelmed with code review. Tech companies are increasingly uncomfortable about token spend with unclear ROI. And it’s becoming clear that even frontier AI models are excellent at task completion but terrible at maintaining codebase quality over time.

So creating complex, long-lived software with AI is still hard. Output may be up, but so are cognitive load, bugs, and incidents. If these companies are Salesforce customers of any scale, I am skeptical that they will also dedicate scarce resources to rebuilding a complex Salesforce implementation in-house. And companies that can feasibly build their own CRM likely never needed Salesforce to begin with, as their requirements are necessarily streamlined enough to have simpler options.

On the other end of the scale, AI is driving a renaissance of personalized, adaptive software. AI now lets anyone generate personal software on demand. Software that adapts to you, not the other way around. And in this area, the challenges associated with AI coding in large, complex systems almost entirely dissolve. The codebase is small and temporary, the blast radius of a bug is low, and the number of people who have to understand the system is limited to a single team or person.

Sharp edges

That doesn’t mean that personal software is without risk. Unsurprisingly, giving powerful tools to a wide new audience comes with its own set of risks. Highly publicized examples include wide-open databases, sensitive applications publicly available and indexed on search engines, and users unknowingly sharing both their codebases and chat transcripts.

And AI and App Builder vendors aren’t helping. When surveying the landscape of AI builders for apps, sites, and artifacts, the same patterns emerge:

• Naive integrations that don’t preserve authentication, authorization, and user context when apps are shared with other viewers
• Mixing trusted and untrusted inputs, and uncritically handing them off as context to AI agents
• Runtimes for untrusted, AI-generated code with wide access to the open internet

Even for a sophisticated developer, these combine to expose a minefield of security risks. Data exfiltration becomes trivial, either through user misconfiguration or agent error, or through well-known prompt injection techniques.

The good news is that all of this is possible to mitigate. Principled, security-conscious app builder tools should extend and enforce the identity and permission layer of apps they integrate with. Agents should practice context hygiene, separate trusted and untrusted input, and never pass business data unnecessarily to AI models. And AI-generated code should always be considered untrusted, and executed only in strongly locked-down runtimes.

From toy to tool

But in a business context there’s still one missing layer. Personal software that exists in isolation can be useful, but is often more a toy than a tool. But when connected to a shared, trusted, and governed organizational source of truth, it enables the best of both worlds. Individual users can create apps and experiences tailored to them or their teams, on top of the shared foundation where the organization remains in control over the overall permissions, processes, and rules that govern how they work.

So if your organization uses Salesforce, you’re in luck. Salesforce has a long history of creating high-quality and widely available documentation, guides, and learning content. As a happy coincidence, this is perfect training data for AI models, and so it is not surprising that agents are extremely good at understanding how a Salesforce org mirrors an organization. At the same time, Salesforce provides a mature set of features that makes it possible to securely build on top of it: consistently enforcing user permissions, clearly distinguishing between data and metadata, and letting agents infer how your organization works based on how it is configured.

I don’t dare to make long-term predictions about where AI is going. But I think there is massive value to be gained in the near term for companies who use Salesforce at scale and who will use Salesforce as the underlying, deterministic layer for AI agents and personal apps. And the enablement goes both ways: personal apps are a superpower that alleviates many of the key challenges to Salesforce adoption. Users can create the exact interface, form factor, or UI they want, while admins or IT remain in control of the underlying data model, rules, and permissions. The question isn’t whether agents need a CRM, it’s how you can realize more value from your existing org by extending it with agents and personal apps.

So I built Wisty.

Wisty bridges the gap between user-generated personal apps and your trusted Salesforce infrastructure. It gives everyone in your organization a secure way to build apps, interfaces and tools on top of Salesforce, always grounded in the data model, permissions and processes owned by your Salesforce org.

Built with security as the first principle, Wisty carefully mitigates the usual risks associated with AI-generated apps. It never gives AI models access to record data, only the limited metadata required to ground apps in your organizational context. AI-generated apps run in secure sandboxes isolated from the open internet, strictly locked down to prevent accidental or malicious exfiltration of data. Apps query record data only at runtime and always respect the actual, current viewer’s Salesforce permissions, even when an app is shared with others.

Wisty understands your org, objects and processes on a deep level, giving you a head start on any app. It smartly searches for relevant objects and fields, analyzing their usage to identify those most relevant to you, before it builds your app.

If your team works in Salesforce, you can generate and use apps directly inside Lightning Experience. Pin your favorite apps as Lightning Tabs, or embed them as context-aware Lightning Components on a Record Page. Wisty even knows how to make apps that blend right in with the Salesforce UI.

Power users, admins and developers working across multiple orgs can create apps from Wisty.ai and reuse them across all your authenticated orgs. Share them with your team at Wisty.ai or publish them to Salesforce users inside any connected org.