loading
loading
the execution layer
A hyperscalable engine, pre-integrated tools, and queryable tables behind one API and one bill. No platform team to hire.
The agent gap
Reasoning picks the next step. Enterprise still needs a system that runs the same workflow safely, visibly, and at scale.
Agents pick the next step. Trickest builds the graph, bursts the fleet, and leaves typed output you can query. Same canvas humans edit by hand.
Same canvas you edit by hand.
Machines appear, then vanish.
Typed rows from tool output.
Dashboards from live output.
One sandbox per agent. Tools, files, and runs stay isolated.
Anonymized outcomes from customers running Trickest at scale.
Financial Technology
Adversary simulation dropped from three weeks to six hours per tactic.
Fast-Moving Consumer Goods
Security testing scaled from eight to thirty tests a month with the same team.
Flagship programs built on the same engine: continuous, scoped, and reproducible.
Connect
$ curl -fsSL https://trickest.io/install.sh | sh$ trickest auth login$ trickest attach$
@trickest/sdk
One npm package, native fetch, no HTTP client to wire up. The whole platform behind a typed API that your coding agent can reach through the CLI, SDK, and MCP.
import { TrickestClient } from "@trickest/sdk"const t = new TrickestClient({token: process.env.TRICKEST_TOKEN,})// Boot a full workflow run on a managed fleet.const run = await t.runs.execute(workflowId, {fleet: "asm-fleet",inputs: { domain: "northstar.io" },})// Re-execute one node. Upstream deps memoized.const partial = await t.runs.execute(workflowId, {node: "httpx",})// Pull a single node's output files.const output = await t.runs.getNodeOutputFiles(run.id,"findings",)
Field notes and docs for teams building on Trickest.
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