The showcase · Technical sheet
- Unsealed
- H1
- 4 sprints
- v0.1.0
- Data as of 2026-10-11
planlang
I planned, built and measured the gap.
- Next.js static export: the showcase: seven screens in Spanish and English, no server
- Strict TypeScript: the deterministic core: plan, verifier, playground and viewer, the same in Node and in the browser
- Python 3.12 · LangGraph 1.2: both demos' agents, with the human pause as an interrupt
- Claude Code by subscription: the agents' model, in batches outside CI; never in the core or the showcase
- Canonical JSON + SHA-256: plans, cases, traces and reports with a fingerprint, checkable byte for byte
- Vitest · Playwright · axe: the tests, including the edge cross-check between Python and TypeScript
What nobody else does
An agent's plan is written as a contract, and a verifier with no AI publishes, case by case, the gap between what was planned and what the agent did, with its failures in view.
16
criteria met across the plans of 2 demos, of 16
measured (Demo A: 10 of 10, in verifier 1.3.0's report on run suscripcion-planlang-a-002-200-v1.5. Demo B: 6 of 6, in verifier 1.3.0's report on run suscripcion-planlang-b-001-20.)1,204
decisions redone in another language, with 0 differences
measured (RF-09.2 cross-check over 4 runs of 2 demos: the TypeScript edge interpreter redoes every decision Python recorded.)220
synthetic cases with a known answer, across 2 demos
measured (Demo A: 200 cases in run suscripcion-planlang-a-002-200-v1.5 and the single-agent baseline. Demo B: 20 cases in run suscripcion-planlang-b-001-20 and the single-agent baseline.)0
model calls when visiting the showcase
declared (Set by the architecture: the showcase is a static export with precomputed data; the package is tested without a single request outside its origin.)5.214US$
nominal cost of running each demo once (220 cases)
computed (Demo A: US$ 4.9206 for 200 cases. Demo B: US$ 0.293 for 20 cases. Sum of the nominal cost the CLI declares per call, over each run's traces; through the subscription it was not paid separately.)
Who it's for, and what it solves
The person before the technology.
Who it's for
For whoever plans, builds or approves an AI-agent project and today measures what the agent does without having written down first what it was supposed to do: they look at traces, run loose evaluations and decide by eye when a result is enough.
The promise
You write the plan as a contract —decisions, risks, assumptions, criteria and thresholds, each one measurable— and planlang uses it to build the agent, run it on cases with a known answer and publish the gap. Then you move a threshold and see what it would have cost.
What it has
6 groups · 20 features.
Plan
4 features
The plan as a contract: the interview, the templates, the validator and the builder's contract.
Run
6 features
Cases with a known truth, both demos and batch runs.
Measure
5 features
The gap verifier and assumptions measured for real.
Play
1 feature
Move the thresholds over the real traces.
Understand
1 feature
The real graph, node by node, against the plan.
Show
3 features
The bilingual showcase and its records.
Limits, and what it never does
What it chose not to be counts as much as what it is.
Limits, on purpose
- It is a simulation: it handles no real cases and decides on no one.
- The interviewer runs in the console, not in the showcase: here you see the plan it drafted for demo B, reviewed and approved by a person.
- It measures what the plan declared; what it did not foresee shows up as a gap, not as a verdict.
Never
- Uses real data: every case, member, physician, applicant and watchlist is synthetic.
- Lets a model decide whether the agent got it right.
- Denies or rejects a case without a person reviewing it.
- Hides an unfavorable result.
Where it stands
The pinned version, not real time.
- H1cycle
- 4closed sprints
- —build closed
- v0.1.0repo version
- 16recorded decisions
In hoja-de-vida, the app's waiting list goes here, and it promises no date. Shown here; not handed over.