TRam Studio

What it is Every agent you build is published as a real API endpoint with generated OpenAPI docs, its own scoped API key, and a complete run history. You send input, the agent executes a defined procedure with a fixed set of tools, and you get structured output back — plus a full record of what happened, step by step, at what cost. The core design An agent is a procedure + a fixed tool list + an endpoint. Tools are declared when the agent is built, never discovered at runtime. If a step names a tool the agent wasn't given, it fails with an explicit "unknown tool" error and is logged — a hallucinated capability becomes a visible failed step, not a wrong action. Agents observe nothing ambient: no filesystem, no desktop, no drifting state. Input arrives on the request or a schedule; every run is reproducible from its inputs. First run Sign up with an email (passwordless magic link), clone one of 23 complete working templates, and you land in a chat workbench with an example prefilled — press send and watch it run. A starter key scoped to that agent is issued automatically. The endpoint exists the moment the agent does; there's no separate deploy step. Building Three modes — Express (describe it in plain English), Guided, and Expert — plus an SOP compiler: paste a written procedure and it drafts the agent, but when the document is incomplete it emits explicit gaps ("step 4 needs an approver — none specified") instead of inventing plausible steps. A human resolves gaps before anything publishes. Capabilities 38 built-in tools: PDF generation and OCR, image OCR, Excel read/write, field extraction, format conversion, JSON validation, HTTP requests, HubSpot and Jira, calculator, QR/barcodes, LLM calls as an explicit logged step, and more. Per-agent knowledge sources with retrieval (RAG), including OCR ingestion for scanned documents. Governance Every run stores a snapshot of the exact procedure text that executed — edit the agent next month, last month's runs still show what ran. Per-step results with errors, per-run cost attribution, actor and channel. Scoped keys are the permission model; workspaces are the tenancy boundary; scheduled runs get the same records as everything else. API POST /v1/agents/{id}/invoke — synchronous (full result in one response) or async with polling. Typed JSON contracts, per-agent OpenAPI, try-it-out console. Engineering ASP.NET Core / .NET 10, ~74,000 lines, server-rendered (no SPA build chain), Docker-ready for Azure or self-hosting, 70%+ test coverage with green CI, published CycloneDX SBOM — 186 components, zero copyleft. Six-language UI (EN/ES/FR/DE/ZH/AR). Multi-model with GPT-5.4 as current default. What it is not No computer-use, no browser driving, no free-roaming autonomy. If you want an AI that watches everything and improvises, this is the wrong tool — on purpose. If your use case is bounded and answerable-for (document processing, intake, validation, scheduled checks, internal desks), it's built precisely for you. Pricing Free to start — no credit card, no sales call. Paid tiers for higher usage. Perpetual non-exclusive white-label licensing for agencies and MSPs (deploy under your brand, source access, modify freely) via tramenterprise.com. Roadmap (in development, labeled honestly): MCP server endpoint so assistants can call governed agents as tools; human-approval steps recorded in run history; one-click evidence export; management API. Built by TRam Enterprise Technologies (Ohio) — founded by a 20+ year enterprise CTO in regulated, high-transaction industries, inventor on two granted US patents. The platform assumes someone will have to answer for what the software did — because in regulated industries, someone always does. https://tramenterprise.com