Conversational to the author. Deterministic to run.
Business automation your users own — without IT surrendering the guarantees it is accountable for. Business users describe rules in plain language. Deterministic code executes them. Every rule compiles to an artifact you can version, review, and audit like code.
Author generatively. Execute deterministically.
Authoring is agentic AI. Execution is not. The conversation produces a structured, coded representation of the rule — and that artifact is what runs, as ordinary deterministic code. AI neither orchestrates execution nor processes your data.
Every automation platform makes you choose the wrong trade-off.
For twenty years, business automation has promised to put automation in the hands of business users. In practice, one generation hands the work back to whoever can think like a developer — and the newer, AI-native generation fixes authoring but breaks execution.
The citizen-developer trap
A canvas, a palette of nodes, a configuration panel — and the work quietly reverts to whoever can think like a developer.
The LLM-in-the-loop trap
AI-native tools fix authoring but sit a model in the execution path — reasoning over live business data, returning a slightly different answer each run.
Describe the rule. Compile it. Run it as code.
Spurgentic splits authoring from execution. The person who owns the policy authors it — in a conversation, with no canvas and no translation step. What runs is the compiled artifact, not the conversation.
Describe — the citizen user, not the citizen developer
Business users say what they want in natural language. No canvas, no drag-and-drop, no nodes to wire, no expressions to learn. The person who owns the policy is the person who authors it — with no developer in the middle.
Compile — the conversation becomes an artifact
Authoring is agentic AI. The conversation produces a structured, coded representation of the rule — a versionable artifact, not a chat transcript. Tokens are spent once here, at authoring, not multiplied across every execution.
Execute — deterministic code, every run
The artifact runs as ordinary deterministic code. AI neither orchestrates execution nor processes your data. Where AI genuinely helps, it is invoked narrowly — one field, one record — and every data operation is code.
Four things you get when AI is out of the execution path.
Authoring generatively and executing deterministically is not a stylistic choice — it is what makes the platform reproducible, cheap, and defensible at the same time.
No drift. No surprise bill. One surface to review.
Because the compiled artifact runs as code, the same rule against the same data yields the same result on every run — and the only place AI touches the system is a single, explicit surface a security team can actually reason about.
Least privilege as an architectural property — not a config habit.
A dedicated data access layer governs every path to data. Configuration covers not just which endpoints may be reached, but which fields within them may be read — and which component is entitled to ask. The boundary is enforced by the architecture, not by trusting a prompt to stay in its lane.
Field-level access control
Configuration reaches inside each endpoint — which fields may be read, and by which component. Least privilege stops being a habit you hope people keep and becomes a property the system enforces.
The authoring engine can compose, but never retrieve
The authoring engine knows only what execution is permitted to reach — tables, entities, schemas. It has the vocabulary to help a user compose a rule and no ability to retrieve a single record.
Denied by configuration, not by trust
Attempt an endpoint call outside what is permitted and the layer denies it — by configuration. Nothing depends on a prompt behaving, or on a model choosing to stay in its lane.
“Who changed this rule, when, and what exactly changed?” has a precise answer.
Because every rule is a structured artifact rather than a conversation transcript, it behaves like code in all the ways that matter to a control function — the one question conversational automation usually cannot survive.
Versioned & diffed
Every revision is captured and diffable — see exactly what changed between two versions of a rule.
Reviewed & approved
Changes route for review and approval before they take effect — a real control gate, not an honor system.
Promoted & rolled back
Rules are promoted across environments and rolled back cleanly — the same lifecycle you already trust for code.
Author, time & rationale
Each change carries who made it, when, and why — a complete record attached to the artifact itself.
Available wherever your users already work.
Spurgentic is not another destination to log into. The same conversational experience is available in Slack, in Microsoft Teams, through Microsoft 365 Copilot, and in a standalone custom web UI — and every channel talks to one core. A rule authored in Teams is the same artifact a colleague reviews in the web UI and the same artifact the engine executes.
Slack
Microsoft Teams
Microsoft 365 CopilotOne conversational core
One rule engine
Identical behaviour
Same rules, same governance, same audit trail — every channel.
The channel is presentation. Policy, execution, and governance live in one place.
Slack and the Slack logo are trademarks of Slack Technologies, LLC, a Salesforce company. Microsoft, Microsoft Teams, and Microsoft 365 Copilot are trademarks of the Microsoft group of companies. Shown here to indicate channel availability only — no endorsement by Slack or Microsoft is implied.
Real automation authority for the business. Every guarantee IT is accountable for.
Generative authoring, deterministic execution, architecturally enforced data boundaries, and audit by construction — four answers to one question. Business users express policy in their own words; IT keeps reproducible execution, a minimal AI surface, field-level control, and a complete change record.