Salesforce delivery has a quiet tax: the repetitive work between having an idea and shipping it. Scaffolding an LWC, wiring an Apex controller, generating test data, writing the boilerplate test class, running the deploy, fixing the inevitable validation error. None of it is hard — but it adds up to most of an engineer's day. On my current team I set out to reclaim that time by building an AI-powered SDLC orchestration platform directly inside VS Code. The result: an 80% reduction in manual engineering effort, with better consistency, not worse.

The core idea: modular sub-agents, not one mega-prompt

The mistake most teams make with AI tooling is reaching for a single, all-knowing assistant. That breaks down fast in an enterprise Salesforce org where context is everything. Instead, I designed the platform around narrow, composable sub-agents — each owning one step of the lifecycle and one well-defined contract:

  • Scaffolder — generates LWC + Apex skeletons that match our naming conventions and design-system tokens.
  • Test author — writes Apex test classes targeting 90%+ coverage, including bulk and negative cases.
  • Reviewer — checks diffs against governor-limit and security best practices (SOQL-in-loops, CRUD/FLS, sharing).
  • Data factory — produces realistic, relationship-aware test data for sandboxes.
  • Release agent — orchestrates Salesforce DX deploys and surfaces failures in plain language.

Because each agent is small, it's easy to evaluate, swap, and keep on the rails. Orchestration scripts chain them together, but any one can be run on its own.

The unlock wasn't a smarter model — it was smaller jobs with tighter contracts and the right context injected at each step.

Grounding the agents in your org

An agent that doesn't know your metadata is a liability. Each sub-agent is grounded with the relevant slice of context — object schemas, existing Apex signatures, naming standards — pulled on demand rather than dumped wholesale. A thin automation layer wraps the Salesforce CLI so agents can introspect the org safely:

# Pull just the metadata an agent needs, scoped to the work item
sf project retrieve start --metadata "LightningComponentBundle:caseTriage" \
  --target-org dev --json | node scripts/index-context.js

Guardrails are the product

Generated code that you can't trust is slower than no code at all, because someone has to read every line with suspicion. The reviewer agent enforces the patterns that matter in Apex — bulkification, no DML/SOQL in loops, explicit CRUD/FLS checks:

public with sharing class CaseTriageService {
    // Bulk-safe: one query, one DML, FLS enforced via WITH SECURITY_ENFORCED
    public static void triage(List<Case> cases) {
        Set<Id> accountIds = new Set<Id>();
        for (Case c : cases) accountIds.add(c.AccountId);

        Map<Id, Account> accounts = new Map<Id, Account>([
            SELECT Id, Risk_Score__c FROM Account
            WHERE Id IN :accountIds WITH SECURITY_ENFORCED
        ]);

        for (Case c : cases) {
            Account a = accounts.get(c.AccountId);
            c.Priority = (a != null && a.Risk_Score__c > 80) ? 'High' : 'Medium';
        }
    }
}

Every generated artifact passes through static checks and the standard test gate before it ever reaches a human reviewer. The AI drafts; the pipeline verifies.

The results

  • ~80% less manual effort on routine build tasks — scaffolding, tests, and data setup that used to eat hours now take minutes.
  • Higher consistency — every component follows the same conventions because the scaffolder, not memory, enforces them.
  • Faster onboarding — new engineers ship correct-by-construction code on day one.

What I'd tell another architect

Start with the most repetitive, lowest-judgment task you have and automate only that. Keep agents small, give them just enough context, and never let generated code skip the gates you'd apply to a human's PR. AI doesn't replace engineering discipline in a Salesforce org — it scales it.

Building something similar? Let's talk — I'm always happy to compare notes on Salesforce + AI tooling.