In modern software development, data is king. But it's also a fugitive, constantly flowing between microservices, third-party APIs, and internal scripts. Every step in a business process is a potential point of failure where data can be corrupted, lost, or mismatched. You've likely seen the fallout: silent bugs that surface weeks later, customer support tickets from broken workflows, and hours spent debugging why a service received a string when it expected a number.
What if you could build a system where data integrity wasn't just a goal achieved through rigorous testing, but a fundamental guarantee of the architecture itself?
This is the power of typesafe function chains. By composing business logic into a series of interconnected, contractually-bound functions, you create a robust pipeline where data is validated at every single step. On a platform like Functions.do, this isn't just a best practice—it's an enforced reality. Let's explore how.
Before we chain functions, let's refresh what makes a single function "typesafe" in the context of a platform like Functions.do. It goes beyond using TypeScript in your editor. It means the platform enforces the data contract at runtime.
You define the exact shape of the data your function expects as input. The platform then takes responsibility for validating every call to that function against your definition.
Consider this simple lead scoring function:
This simple guarantee is powerful. It eliminates a whole class of runtime errors, improves developer productivity, and ensures data is clean before your business logic even runs.
Now, let's scale this up. A single function is useful, but most real-world business processes are multi-step workflows.
Imagine a typical lead processing pipeline:
Each arrow in that flow is a potential point of data corruption:
In a traditional microservices architecture, you'd manage this with manual error handling, data validation libraries in each service, and extensive integration tests. It's fragile and complex.
With a typesafe function chain on Functions.do, you model this workflow as a series of connected, composable functions where the output of one becomes the contractually validated input of the next.
Let's model our lead processing pipeline:
This function's only job is to ingest raw data and sanitize it.
This function takes the clean, normalized lead and adds external data.
Our original function now works with richer data.
On the Functions.do platform, you orchestrate these functions into a single, cohesive workflow. The magic is what happens between the steps: the platform acts as the typesafe arbiter. If enrichLead fails or returns data that doesn't match the EnrichedLead interface, scoreLead is never called. The workflow halts, an error is logged, and bad data is stopped dead in its tracks.
This "chain of custody" for data provides three incredible advantages:
Immediate Error Isolation: A problem in one function doesn't create a cascading failure of corrupted data. The error is isolated to its source, making debugging dramatically faster. A change in the webhook format only breaks the first function, not the entire system.
Extreme Composability: Your scoreLead function is now a pure, reusable piece of business logic. It doesn't care if the data came from a webhook, a CRM, or a manual entry, as long as it receives a valid EnrichedLead object. You can swap out the enrichment service or the ingestion method without touching the core scoring logic.
Fearless Maintenance: Need to refactor the enrichLead function? Go for it. The typesafe contract acts as your integration test. If your changes break the EnrichedLead output type, the platform will immediately flag the incompatibility with the scoreLead input during deployment, long before it causes an issue in production.
This concept of chaining is the foundation of what we at Functions.do call Agentic Workflows. It’s more than just a simple pipeline. By allowing functions to be chained, managed, and orchestrated, you're creating a system that can execute complex, multi-step business processes as a single, reliable software service. The platform abstracts away the boilerplate of state management, retries, and error handling between functions, letting you focus on the "Business-as-Code" that delivers value.
Stop chasing data consistency bugs and start building resilience directly into your architecture. With typesafe function chains, you can build systems where data integrity isn't an afterthought—it's an automatic guarantee.
Ready to build your first unbreakable workflow? Discover Functions.do and transform your business logic into reliable, production-ready services.
import { Function } from 'functions.do';
// Define the input type for your function
interface Lead {
companySize: number;
industry: string;
hasBudget: boolean;
}
// Create a function agent with a defined input type
const scoreLead = Function<Lead>('score-lead-v1');
// This call succeeds because the input matches the Lead interface
const { score, reason } = await scoreLead({
companySize: 250,
industry: 'SaaS',
hasBudget: true,
});
// The platform would automatically reject this call before execution
// because 'companySize' is a string, not a number.
// await scoreLead({ companySize: '250', ... });
// Output type for the first function
interface NormalizedLead {
id: string;
email: string;
domain: string;
}
// Output type adds new fields from enrichment
interface EnrichedLead extends NormalizedLead {
companyName: string;
companySize: number;
industry: string;
}
// Final output from the scoring logic
interface ScoredLead extends EnrichedLead {
score: number;
reason: string;
tier: 'Enterprise' | 'SMB';
}