Why auto-documentation
Every workflow you build is also a decision: which agents handle what, which tools they’re allowed to touch, where humans step in, what errors get retried. Over time that intent lives only in the canvas, and newcomers — reviewers, auditors, teammates taking over an on-call — have to reverse-engineer it from nodes and prompts. MagOneAI can read a use case and produce a structured, human-readable document that explains the workflow in plain language. It’s always derivable from the current workflow, so it stays accurate without being maintained separately.Auto-documentation is descriptive, not authoritative. The workflow itself is the source of truth — documentation is a view on top of it.
What gets generated
The generated document is organized into sections that mirror how a reviewer actually reads a workflow — starting with the big picture, then drilling into specifics:Overview
What the workflow does, how it’s used (chat, form, or hybrid), and which agents or tools make it work.
How to use
The expected inputs, how to trigger the workflow, and what consumers should do with the output.
Flow
Step-by-step narration of the workflow — each activity, how it’s connected, and what routes it takes.
Agents
Each agent’s role, persona, and the tools and knowledge bases it can access.
Integrations
External tools the workflow connects to, with setup notes for integrations that require credentials.
Error handling
How failures are handled — retries, fallbacks, and where execution can halt or route around errors.
Execution summary
Recent run statistics when available: volume, success rate, latency, and where failures cluster.
Governance
Guardrails in use — output schemas, prohibited-content checks, human approval points.
How it works
Generation happens on demand. Open the Documentation panel for a use case and trigger a new generation — the platform reads the current workflow and the agents it references, then produces the document.Generate
The platform inspects your workflow and drafts each section. Generation is powered by your selected language model and takes a few seconds.
Review
Read the generated document inline. If something’s off, tweak the workflow — rename a node, sharpen a prompt, update an agent’s persona — and regenerate.
Staying fresh
Documentation is cached after generation, so opening the panel again loads the last version instantly. If you edit the workflow afterwards, the cached doc is automatically flagged as stale — a one-click refresh brings it up to date.Deterministic fallback
When a language model isn’t available or you want a baseline view, MagOneAI also supports a template-based renderer that produces the same sections from the raw workflow — no LLM needed. The content is more mechanical but always consistent, which is useful as a sanity check against the AI-written version.Exporting
Three output formats cover most needs:- Copy to clipboard — paste into Slack, a PR description, or a wiki page.
- Markdown (
.md) — check into a repo alongside the workflow export, or feed into your own documentation pipeline. - PDF — formatted for sharing with non-technical stakeholders; optionally includes the canvas diagram so the reader can see the workflow shape.
Good uses for auto-documentation
- Onboarding. A new engineer inherits a workflow. Read the generated doc first, open the canvas second.
- Review packets. Attach the PDF to a change request so reviewers see intent alongside the diff.
- Audits and compliance. Show reviewers what a workflow does, which tools it touches, and what guardrails are in place — without them needing Studio access.
- Cross-team handoffs. Export the markdown with the use case so the receiving team has context, not just configuration.
Next steps
Versions & publishing
Regenerate documentation as part of the publish process to keep what’s written and what’s running in sync
Workflow overview
Back to activities, the canvas, and workflow basics