What GTM Workbench is
GTM Workbench is an AI-assisted workspace where agents and workflows turn your source material — sales-call transcripts, documents, URLs — into publishable artifacts: case studies, one-pagers, emails, LinkedIn posts, and briefs. It is deliberately human-in-the-loop (HITL). The agents handle analysis and first drafts; you curate, approve, and refine at every stage. This is intentionally not a black-box, fully automated pipeline — you stay in control of what ships.Workbench is a flagship example of what teams build on the Corbits agentic platform. We run it internally to move our own go-to-market work — so the platform primitives it depends on (tenancy, agent lifecycle, credentials and grants, native workflows, and skills-as-assets) are proven in production, not just documented.
Meet Myra, your chief of staff
Every user gets a personal AI agent named Myra, framed as a chief of staff and executive assistant. Workbench opens directly into a conversation with her — it’s chat-first.Parallel threads
Run multiple Myra threads at once. Each thread is a full agent with its own tools and history — not just a saved transcript — so you can keep separate lines of work going in parallel.
Shared memory
Myra’s durable memory is shared across all of your threads. What she learns in one chat — standing facts, context, contacts — is available in the others.
Works with your files
Attach an image or a PDF to a message. Myra reads images directly and understands PDFs through a dedicated file parser, so documents work regardless of her underlying chat model.
Reuses your artifacts
Myra can read a document you or a workflow saved earlier as an artifact, so prior work becomes input for the next task.
Alongside Myra, a workspace agent named Oat processes call recordings into call-document artifacts, giving your workflows clean source material to build on.
The source-to-artifact model
Workbench follows one product rule: you choose outcomes, not pipeline topology. You bring source material, pick the outcome you want, review the decisions that matter, and approve usable artifacts. The internal steps stay hidden. That model is built from a small, consistent vocabulary:| Term | What it means |
|---|---|
| Source | Input material you select — a transcript, a document, a URL, or a prior Artifact reused as input. |
| Offering | The product-facing outcome you pick from a catalog or command menu, such as “Create sales collateral” or “Draft LinkedIn posts.” |
| Job | One run of a workflow against your selected sources and options. |
| Review Gate | A human decision point — confirm sources, select findings, approve artifacts, confirm delivery. |
| Artifact | An output produced by a Job. An Artifact can later become a Source for the next Job. |
| Package | A curated group of artifacts you review, export, or deliver together. |
| Hook | An optional final delivery step — copy or export, draft an email, or draft and schedule a social post. |
Choose your source
Select the material a Job should work from — call documents, uploaded files, URLs, or an artifact from earlier work.
Choose an outcome
Pick an Offering that describes what you want to produce. You select the outcome; the workflow decides how to get there.
Review findings
At a review gate, the Job shows you what it found and asks for the decision that matters — which findings should drive the content.
Approve artifacts
Approve, reject, or request refinement on each generated artifact before it counts as done.
Every Job streams its progress in a single, generic run console. There is no bespoke per-workflow screen to learn — the same review surface handles confirming sources, selecting findings, and approving artifacts across every outcome. When a Job reaches a review gate, it pauses and waits for your approval before continuing.
Skill Library
Upload, browse, and version reusable AI instruction sets — the prompts, personas, and procedures that shape how agents respond. Inspect a skill’s files, review its version history, and restore any prior version. Attach a skill to an agent to change its behavior.Built on the Corbits platform
Workbench doesn’t reinvent the hard parts. It relies on the Corbits agentic platform for the primitives every serious agentic product needs, so the team can focus on the go-to-market experience instead of infrastructure.Tenancy and agent lifecycle
Tenancy and agent lifecycle
The platform provisions each user’s personal Myra instance and manages the lifecycle of every agent and workspace worker. Workbench doesn’t build its own identity or agent-runtime layer.
Credentials and grants
Credentials and grants
Inference and tool credentials are stored and resolved by the platform, and every tool call is authorized against a grant. Members never enter API keys, and access is scoped by the platform rather than a hand-rolled permission model.
Native workflows
Native workflows
Workflows are deployed definitions that run on the platform’s native workflow runtime — durable runs, review-gate signals, and event logs included. Adding a new workflow is a new package and a push, not a rewrite of the app.
Skills as assets
Skills as assets
The Skill Library is backed by the platform’s asset substrate, which owns storage and versioning. Workbench never reimplements how skills are stored or versioned.
Platform overview
See the agentic platform Workbench is built on.
Credentials and guardrails
How credentials, grants, and delivery capabilities are governed.
Why teams use it
Fast
Select your sources, pick an outcome, and get draft collateral in minutes.
Reviewable
Every stage is human-approved. No black-box automation deciding what ships.
Resumable
Jobs and sessions are saved, so you can step away and come back to iterate.
Exportable
Approved artifacts are ready to copy, download, or deliver through a Hook.