AI understands. Deterministic code decides.
Milita uses a model to interpret what you say, to read your org, to choose the next question, and to explain what she found. Whether a Promise held comes from a real run in a clean session against your real sandbox, graded by typed assertions on business results. A model's opinion never becomes a verdict.
Eight rules the code obeys.
AI understands. Deterministic code decides.
The model interprets, proposes and explains. Typed assertions against real org state decide. There is no path from a model's confidence to a green result.
Plans are data, never code.
Everything Milita builds is a validated plan of known primitives, a strict closed schema with a fixed instruction set. The model cannot emit browser code. A missing capability becomes a named platform gap, never an escape hatch. There is no customer-code path and no staff-code path; the server assembles every bundle from its own harness plus the validated plan, and the runner verifies the bytes it runs against the published version.
Steps are generated, not improvised.
When Milita proposes a Promise from your org, deterministic generators build every step from what was read. The same input produces the same plan.
Missing can never become passed.
A missing results file, an unknown status, an empty collection, an exit code of zero without a result contract: each of these is an error, never a pass. This is a build-time invariant with tests behind it.
Watching never counts.
A Debug you watched, a demonstration, a recording: these teach. Only her own clean cloud run, with fresh data and no help, counts as Proof.
Consequential actions are held.
A destructive, permission, credential, or external action is never executed on the model's decision. You accept it from a card, same user, same tenant, within ten minutes.
She is measured.
Milita is re-measured every night against an evaluation corpus: authoring fidelity, questions asked, false assumptions, plan validity, injection resistance. A new model is admitted on evidence, by a documented procedure, with one-click rollback. A skipped evaluation can never look like a pass.
Independence is the feature.
An autonomous agent that navigates your org by sight and declares success is a second non-deterministic actor that itself needs verifying. Buyers should distrust any vendor grading its own agents' homework on a similarity rubric.
AI writes faster than anyone can test.
Code arrives at a new speed
Code and configuration now arrive at a velocity nobody has seen before, from humans using AI tools and from agents acting on their own. That work reaches sandboxes and production at the same speed. Testing cannot keep pace by adding people.
Self-testing is a conflict of interest
Give a model the job of building the change and the job of passing the check, and it will find a way to pass: it will loosen the assertion, work around the failure, or declare success on the wrong evidence. The answer is a separate system, deterministic, independent of the thing it checks, that grades the real result in the real org.
Salesforce is becoming non-deterministic
Agents, MCP tools, a headless platform: the same process can now be driven by a person, by an integration, or by a model deciding on its own. "Passed once" is worthless against a non-deterministic actor. You need repeated, real-environment verification of business outcomes from someone who is not grading their own homework.
What the model sees, and what it never sees.
Every model call is assembled server-side from your own rows. The model never touches the database; every read it makes replays through the real routes with your own session, so tenant isolation and authorization apply to every read.
- Sees: names, labels, values and outcomes on the pages and the structure she reads, bounded and fenced as data.
- Never sees: Salesforce credentials, API keys, run evidence, formula text verbatim, or another customer's rows.
- Served by Google Vertex AI, not used to train models, prompt logging on Google's side off.
- Tool results are capped, fenced as data, and sanitized before they reach a prompt.
Against the alternative
Platform-native agent testing scores semantic similarity, not the resulting org state, which means it can mark a hallucinated answer as passing. Autonomous visual testers self-report navigation benchmarks that independent reproductions land far below. Both are models grading models.
Milita grades the resulting business state, repeatedly, in the real sandbox, independently. The AI proposes; the run decides.
Compare the alternativesDon’t tell me it works.
Prove it.Free for 3 Promises, forever. See a deterministic Proof of your own process.