How to add AI agents to Infor SyteLine
Short answer
Build AI agents on SyteLine by giving a model a small set of tools: read tools over IDOs or a read-only SQL Server replica, and write tools that only create drafts or requests a planner approves. Keep the agent outside the application tier, authenticate as a dedicated SyteLine user so existing authorizations apply, and log every tool call.
Most SyteLine shops run a long tail of manual chores: chasing late purchase orders, working the planning exception list, re-keying customer change requests, and explaining to a supervisor why a job is short on material. The data to do all of it already lives in SyteLine, but it is spread across forms, the Application Event System, and a few reports that only two people know how to read.
An AI agent is the layer that does the multi-step reading and drafting. It looks at an exception, pulls the related job, PO, and inventory records, decides what the likely cause is, and prepares an action for a human to confirm. The value is not a chatbot answering questions; it is a worker that finishes the first 80 percent of a routine task and hands over a reviewed draft.
SyteLine is a good target because its Mongoose framework exposes business logic through IDOs (Intelligent Data Objects) rather than forcing you to write directly to tables. That means an agent can read through the same objects the forms use and, when writes are allowed, go through the same validation. Exact IDO names and available methods vary by version and by customization, so treat the examples below as the pattern, not a catalogue.
What teams use it for
Late purchase order expediting
The agent scans open PO lines past their due date, checks which jobs and customer orders depend on them, and drafts a supplier email plus a ranked list of which orders will slip first.
Planning exception triage
After an MRP or APS run the agent groups exception messages by cause (missing lead time, changed demand, scrap), proposes reschedules, and leaves the planner a short list instead of several hundred rows.
Draft NCRs from shop floor notes
Given an inspector's free-text note and the job, item, and lot, the agent drafts the nonconformance with the right references and suggests disposition options for the quality engineer.
Customer order change handling
When a customer emails a quantity or date change, the agent finds the order lines, shows the impact on jobs and purchasing, and prepares a change for the CSR to approve.
Month-end accrual and receipt checks
The agent compares received-not-invoiced and goods-in-transit balances to open receipts and flags lines with no matching PO, so accounting reviews exceptions instead of every line.
Cycle count variance research
For each large count variance the agent pulls recent inventory transactions for the item and location, looks for unposted moves or unit-of-measure issues, and proposes the likely cause.
Infor SyteLine (CloudSuite Industrial) integration surface
- IDOs through the Mongoose IDO runtime: the Load Collection request returns rows from an IDO with filter and property lists, and IDO methods run business logic. On Infor OS / cloud deployments this is typically reached through the ION API gateway with OAuth service accounts; on-prem installs expose the IDO web service directly (REST and, in older versions, SOAP). Check the endpoint style your version supports.
- The SQL Server application database. Tables carry the _mst suffix (for example item_mst, job_mst, jobmatl_mst, po_mst, poitem_mst, co_mst, coitem_mst, matltran_mst) and almost every row has a site_ref column because SyteLine is multi-site. A read-only replica or a reporting login is the safest source for broad analytic reads.
- The Application Event System (AES): event handlers fire on IDO events and can call actions, including a web request. This is the clean way to trigger an agent when a record changes without polling.
- ION and BODs: where the customer has Infor ION, SyteLine publishes and consumes BODs for documents such as purchase orders and sales orders. BODs are a decoupled way to receive change notifications.
- Utility and background features: scheduled background tasks, report requests, and user-defined methods (custom IDO methods written in .NET) that can wrap tricky logic so the agent calls one safe method instead of many.
How it works
- 1
Pick one workflow and define done
Choose a task with a clear input and a reviewable output, such as drafting expedite emails for late POs. Write down what the agent may read, what it may propose, and what a human must approve.
- 2
Create a dedicated SyteLine user
Make a service user with the minimum authorizations for the forms and IDOs involved. The agent inherits that user's rights, so SyteLine's own security remains the boundary.
- 3
Build a thin tool layer
Wrap each capability as a narrow function: get_open_po_lines, get_job_material_status, get_item_availability. Each tool calls an IDO load or a parameterized query and returns compact JSON, never raw SQL access for the model.
- 4
Add write tools as drafts
Writes create something reviewable: a planner note, a requisition, a change request record, or an email draft. If a direct IDO write is justified, run it only after an explicit approval step and log the payload.
- 5
Wire triggers
Use an AES handler, a ION BOD subscription, or a scheduled job to start the agent. Pass only record keys; let the agent fetch current data through the tools.
- 6
Add evaluation and logging
Store every prompt, tool call, result, and approval. Replay a sample of past exceptions with known answers before going live, and review a weekly sample afterward.
Example flows
Late PO expediting
Trigger: Nightly schedule at 5 a.m.
- Tool lists PO lines with a due date earlier than today and open quantity.
- For each line, tool finds jobs and customer order lines that depend on the item.
- Agent ranks lines by the earliest dependent ship date.
- Agent drafts a supplier email per vendor and a summary for the buyer.
- Buyer reviews, edits, and sends from the usual mail client.
Result: Buyers start the day with a ranked, pre-drafted list instead of building it from a report.
Short job material triage
Trigger: AES event when a job is released with a material shortage.
- Agent loads the job and its material lines with quantities required versus on hand.
- Agent checks open POs and transfer orders for each short item.
- Agent proposes: wait for PO, substitute item, or expedite.
- A planner approves one option, which becomes a note and a task.
Result: The planner sees cause and options on release day rather than discovering the shortage at the work center.
Keeping data on-prem
SyteLine is frequently run on-premises, and many of those plants cannot send part numbers, customer names, or drawings to a public API. The agent pattern works the same with an open-weight model (a Llama, Qwen, Mistral, or gpt-oss class model) served with vLLM or Ollama on a GPU server inside the plant network. The tool layer, the agent service, and the model all sit in the same network segment as the SQL Server replica, with no outbound path required. Size the GPU to the model and the number of concurrent agents; a small number of daily batch agents needs far less than an interactive assistant. Keep the model host patched and isolated, and treat the agent service as a privileged integration.
Security checklist
- Use a dedicated SyteLine service user and give it only the authorizations the workflow needs.
- Expose tools, not SQL: the model should never write its own queries against production tables.
- Require human approval for any write to orders, purchasing, inventory, or financial records.
- Log every tool call with user, parameters, row counts, and timestamp, and keep the log outside the agent service.
- Restrict outbound network access from the agent host, and scrub credentials from prompts and logs.
Ways to get it done
Infor's own automation (Infor Process Automation and Infor AI features)
Effort: Weeks, depending on licensing and skills.
Good when the process is rule based and the customer already licenses Infor OS capabilities. Less suited to reading free text or reasoning across many records.
Build in-house on an agent framework and IDO tool layer
Effort: Two to four months for a first workflow, then incremental.
Fits teams with .NET or Python developers and a SyteLine expert who can define safe tools and test cases.
Work with a partner such as Netray (ERPray and SyteRay)
Effort: Typically a scoped pilot over several weeks.
Useful when you want private, on-prem models and prebuilt SyteLine tool patterns, with a SyteLine functional expert on the project.
Pitfalls
- !Letting the model write SQL against live tables: slow queries, missing site_ref filters, and wrong joins across sites.
- !Ignoring customizations: user-defined fields and custom IDOs mean the same question returns different columns at different customers.
- !Automating a broken process: if planners disagree on what a good reschedule is, the agent will reproduce the disagreement.
- !No stop condition: agents that loop on a failing tool call burn tokens and flood the audit log.
- !Skipping the pilot review: accuracy on the first 50 exceptions is the real approval criterion, not a demo.
Frequently asked questions
Can an AI agent write to SyteLine?
Yes, through IDO methods or custom methods, but start read-only. For writes, have the agent create drafts or requests that a user approves, and run the final write as a service user with limited authorizations so SyteLine validation and audit still apply.
Do I need Infor OS or ION to do this?
Not necessarily. The IDO web service and a read-only SQL Server replica are enough for many agents on-premises. ION and BODs are helpful for event triggers and cloud setups, but they are not a hard requirement for a first workflow.
Which model should the agent use?
It depends on data sensitivity and task difficulty. Frontier cloud models reason well on messy exceptions; open-weight models running on your own GPUs are often adequate for structured tasks and keep data inside your network. Test both on your own historical cases.
How is this different from the SyteLine reports I already have?
Reports show data. An agent follows up: it pulls the related records, explains a likely cause, and drafts the next action. If a report and a filter already solve your problem, you do not need an agent.
How long does a first agent take?
A single, well-scoped workflow with read tools and draft outputs usually takes a few weeks to build and a few more to evaluate. Broad, multi-department agents take longer, mostly because of process agreement and testing rather than code.
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