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In this guide you’ll build a miniature order-matching process: order documents come in, an agent matches them against your customer master data following an SOP, and hands you evidence-backed cases to approve. You’ll touch every core piece: a project, master data, a reconcile table, an SOP the agent helped write, and the review flow.
You’ll need a cloudsquid account and two kinds of sample data: a handful of order documents (PDFs or email files) and a small customer list as CSV. No integrations required for this guide.

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1. Create a project

Everything lives in a project: your files, your tables, your SOPs, and the agent that works them.
  1. From the home screen, click New and name the project (e.g. Order Entry Sandbox).
  2. You land in the project workspace: Files and Tables in the sidebar, the agent available bottom-right.

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2. Add your master data

The agent matches incoming documents against something — that something is a storage table.
  1. Create a table and choose Storage , name it Customer Master Data.
  2. Upload your CSV of customers (number, name, address, email).
  3. Column names matter: the agent reads the schema to understand your data — Postal_Code beats col_7. Add column descriptions where names aren’t self-explanatory.

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3. Create the reconcile table — where the process lives

This is the core of cloudsquid: a reconcile table turns events into worked cases.
  1. Create a Reconcile table, e.g. Order Match.
  2. In Configure, set up:
    • Operating Procedures — keep it to one line: Match incoming orders according to the SOP. The real logic goes in the SOP file (next step).
    • Output Fields — what the agent should produce per case: e.g. customer_match_status, customer_number, order_number, contact_email, delivery fields. Give each field a clear description — the agent reads them.
    • Data Access — grant the agent read access to Customer Master Data. The agent can only touch what you grant.
    • AI Model — start with Balanced; switch to Powerful for hard documents.

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4. Write the SOP — with the agent

SOPs are plain Markdown files in your project. You don’t write them from scratch:
  1. Open the agent chat and ask it to draft one:
    Look at Customer Master Data and the order files in the project. Draft an SOP.md for matching incoming orders to customers: match on buyer name + address first, use email as a tiebreaker, and flag anything ambiguous for review instead of guessing. List open questions you can’t resolve from the data.
  2. The agent writes SOP.md — real procedure, not prose: match steps, fallbacks, when to stop and escalate. Where it isn’t sure, it lists open questions for you.
  3. Answer what you can, ask it to revise. The SOP carries a version stamp and the latest approver, so process changes stay reviewable.
This loop — agent drafts, your process expert corrects, the file versions — is how real deployments onboard. Teaching, not configuring.

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5. Run the process

  1. Upload two or three order documents to the project’s Files (or drop them straight into the reconcile table).
  2. Add a task per order. The agent picks each one up: reads the document (vision included), queries your master data, applies the SOP’s match steps, and fills the output fields. Status moves Running → Agent Done.

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6. Review the case

Open a finished task. This is the review interface — where your controllers will live:
  • Source document on one side, the agent’s comment on the other: what it matched, how, and why.
  • If the agent couldn’t resolve something safely, it says so and lists the candidates instead of guessing. That case gets Needs review — that’s the system working, not failing.
  • Approve the case, or reject it with a comment — the agent reads comments.
Once you trust a process, routine approvals can be handed to an approval agent that reviews results against the SOP; you keep the exceptions. And as review comments accumulate, the agent can propose SOP updates based on them — see the improvement loop.

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7. Bonus: ask for a report

The agent has a computer, not just a queue — it can produce deliverables about its own work. In the agent chat:
Summarize the cases in Order Match as a short report: how many matched automatically, what needed review, and why.
It analyzes the table and writes the file into the project’s Files. Real deployments get their weekly automation reports exactly this way — on demand, not as a scheduled job.

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Where to go next

Cookbooks

Full recipes with copy-paste starter SOPs: PO matching, invoice audit, inbox intake.

Connect real inputs

Trigger this process from a shared inbox, Zendesk, or SharePoint instead of manual upload.

Need structured data first?

For high-volume document batches, an extraction table structures files before reconciliation.

Review & approvals

Statuses, assignment, agentic approval mode.