BOOMBOX · WHAT IT IS FOR
When to use Boombox.
Boombox is the developer kit and runtime that turns systems your company already owns into AI-native products your customers can use. This page says plainly what it is built for, and where it is not the right tool, so you can judge the fit in one read.
What you would use it for.
Eight situations, stated concretely. If one of them is yours, Boombox was built for it.
01
Turn an internal system into a product customers can call.
A model, a dataset, or a workflow already earns its keep inside the company, and a customer wants to use it. Between those two points sits packaging, permissions, evaluation, deployment, and versioning. Boombox composes the product from what is already in the repository, writes eval cases from the cases the team already knows the answers to, and ships once the gate passes.
Example: a margin model, an accounts API, and a Slack approval flow become one margin-check product.
02
Let agents use your systems without handing them over.
Every build is a tool agents can call, including an MCP service, the interface agents already speak. Your own agents use it. Another company’s agent can pick it up mid-task, under a grant your company approved. Permissions, evaluation, and a record come attached, and refusal is a first-class answer.
03
Build it once. The frontend, the API, and the agent tool come with it.
You write one build. A frontend, an API, an MCP service, an agent tool, and a scheduled workflow come with it, and not one of them is a second build. Your systems stay underneath, where they are.
04
Put an evaluation gate in front of every AI release.
Cases come from real incidents and from the answers the team already knows. The gate runs itself: a build that fails its cases does not ship, and approval is the only human step. Every run is recorded, every miss becomes a case, and the loop tightens itself.
05
Run your product inside your customer’s environment.
A build can run in your cloud, in a Konstant-managed environment, or inside a customer’s environment, where their approval opens the edge. Identity, provenance, evaluation, attribution, and value stay attached wherever it runs.
06
Break an existing app into pieces agents can safely reuse.
Split an application into pieces small enough to understand. A piece that small can be tested and approved on its own, and approved pieces are what agents can safely combine into new workflows. Boombox attaches evaluation to each piece and keeps every new combination under the same approvals.
07
Your AI feature shipped without a record.
The vibe-code bill comes due: evals, provenance, and receipts rebuilt after the fact cost quarters, not weeks. Boombox writes the record from the first run: cases written from answers your team already knows, a gate that does not let a failing build ship, a receipt for every action an agent took, and refusal as a first-class answer from day one.
08
Your product could be someone else’s capability.
One build answers inside another company’s agent session, under your grant, with your evidence attached. Cross-company use without losing custody.
Where this fits.
Evaluation and observability tools measure your AI product; your team still runs the loop between them by hand: choosing thresholds, reading dashboards, wiring CI, clicking promote. Boombox systemizes that loop, and it makes evaluation native to every piece of the product from inside, instead of attached to the whole from outside.
| the step | you may already have | with Boombox |
|---|---|---|
| trace and observe | observability platforms (Langfuse, LangSmith, and friends) | keep them; results write back into yours |
| an eval framework you already own | BrainTrust and friends | keep it; Boombox houses whatever scoring you run under one gate and one ledger. Be the house, not another evaluator |
| define what good is | dashboards and datasets a person curates | eval cases written from the cases you already know the answers to, bound to each piece |
| run the checks | test scripts a person wires into CI | the gate runs itself; a build that fails its cases does not ship |
| decide to ship | a person reads results and clicks promote | the decision is the gate; approval is the only human step |
| deploy | your pipeline, one environment at a time | one build answers as frontend, API, MCP, agent tool, schedule, wherever approved |
| learn | postmortems and dashboards | every run recorded; every miss becomes a case; the loop tightens itself |
If you already run one of these, keep it. We run Langfuse ourselves. The seam is standard: your traces fan out from your own collector, your eval cases move as plain files, and every one of these tools accepts a score written back by someone else. What is already stored stays where it is; nothing migrates. The past is sticky, the future is portable, and the loop only needs the future.
Where Boombox does not fit.
The honest edges
It is not an observability platform. Teams that run Langfuse, LangSmith, or similar keep them; Boombox writes results back into them.
It is not a dashboard a person reads. The gate replaces the read-and-promote step; your observability tools remain the place to look.
It is not a generic web framework. It earns its place where evaluation, permissions, and deployment across company boundaries are the actual work. The docs state the current product boundaries directly.
Start in your own repository.
npm install --save-dev --save-exact --ignore-scripts @konstantdotcloud/boombox then node ./node_modules/@konstantdotcloud/boombox/dist/boombox.js project init . The plan it writes is a description, not a change. It reads to a security team without a translator, and it stays yours whether or not you ever open an account. The local kit is free: the skill, the runtime, the evals, the plan.
Full guide: Building on Boombox. Conversation: pilot@konstant.cloud.