AI Rule Engine Journal
Keep up with the latest announcements, tutorials, demos, and product notes from AI Rule Engine.
A rule set that fires no rules gives you no error and no output to work backwards from. A goal-driven run turns that silence into a tree, where the deepest red node names the input that was actually missing.
A goal-driven ruleset does not have to answer the same question every time. Override its goals per run, from the runner or from an API call, and one set of rules can answer eligibility today and pricing tomorrow without being edited.
Name the context keys a run should establish and the engine works backwards to find the rules that can produce them, firing only what the answer actually needs. Every run comes back with a proof tree that shows how the value came to be, or which input was missing.
Every mature ruleset accumulates logic that no longer does anything. Here is how to find it in an afternoon, using the fire rates on the Analytics page and the Verify tab, and how to tell dead rules apart from rules that are merely waiting.
The new Analytics page shows how your rules behave in production: how often each rule fires, what decisions your RuleSets are producing, and how runs are turning out day by day.
A reference view for cloud architects: run AI Rule Engine in your own Azure subscription, point AI steps at your own Azure OpenAI resource, and expose governed decisions to AI agents over MCP - with the data boundary drawn where your compliance team expects it.
Give any rule an active date window and a policy change turns itself on and off. Schedule a price change weeks ahead, retire a promotion at midnight, and prove both work with a simulated run date in your tests.
Mark a rule as retractable and its conclusions are only true while the rule still matches. When the facts behind a decision move, the decision comes back out, and so does everything built on top of it.
The new Verify tab reads your RuleSet without running it and tells you what is wrong: rules that can never fire, rules that duplicate each other, two rules fighting over the same value, keys nothing supplies, and input combinations no rule covers.
Thousands of MCP servers hand your agent data. Almost none hand it decisions. AI Rule Engine turns your business logic into governed, deterministic MCP tools - with per-agent permissions, explainable runs, and human approval gates an agent can carry but never bypass.
Workflows that pause for human review no longer dead-end an AI agent. Over MCP, your assistant can read the pending form, submit your answers on your behalf, and bring back the final result — with a per-action switch that keeps in-person approvals in person.
A tour of what shipped this month — forward-chaining inference, explainable traces, what-if simulation, derived facts, the expression language, and collection conditions — with a link to the full story on each.
Turn on Inference and your rules re-fire as their actions change the facts other rules read — one firing at a time by salience, only where inputs actually changed, until the run reaches a stable state.
Most engines tell you the result. The inference trace tells you the reasoning — every firing in order, a green/red condition tree with the actual values, every context change, and a plain-language explanation on demand.
Edit the inputs and hit Compare. AI Rule Engine runs your RuleSet twice in the sandbox and shows exactly which rules and conclusions changed — each traced back to the input that moved it. No metered run, no side effects, nothing in your history.
Stop passing in values you could compute. Derived facts are context values resolved on demand from other values — defaults, shared definitions, expressions, even lazy AI classifications — evaluated only when a rule actually reads them.
A small, deterministic expression language for computed values and conditions: read context with ctx.key, 32 built-in functions across strings, math, dates and arrays, null-safe with coalesce, and validated as you type.
Aggregates and quantifiers let a single condition reason about a whole array. Sum the line items and compare, or ask whether any element clears a threshold — no extra rules, no manual unrolling.
Why the expression language has no now() or random(), why re-fires key off real value changes, why AI results are remembered for unchanged inputs, and why an inference cascade still meters as one run. Determinism is what makes the rest trustworthy.
The RuleSet editor has been rebuilt around a DMN-style decision table: every rule is a row, every context key is a column, and you can author conditions and outputs inline without opening a single dialog.
AI Rule Engine now includes native support for Azure OpenAI models, and when paired with our dedicated Azure hosting plan, your data never leaves your own tenant.
Token-maxing every decision sounds like a smart AI strategy until the bill arrives. Here's how to use AI where it earns its cost and let rule-based logic handle the rest.
AI Rule Engine now lets you create a new project by describing what you want to automate. The AI generates the rules, conditions, and actions that make up your workflow — no blank canvas required.
AI Rule Engine extensions now include a built-in rich text editor, letting you add formatted instructions, embed images, collect user input with a variety of field types, and control UI visibility with conditional blocks.
AI Rule Engine now includes a Human Intervention action that lets you design rich-text forms and collect user feedback in the middle of an automated workflow.
Different AI models excel at different tasks. AI Rule Engine lets you mix providers and models in one workflow so you can optimize for quality, speed, and cost at each step.
AI Rule Engine now includes native support for Anthropic AI models, making it easier to build governed workflows with Claude and other supported models.
AI Rule Engine now includes a visual workflow editor for exploring branches, editing nodes inline, and saving workflow drafts from an interactive canvas.
AI Rule Engine offers shared-host plans (Free, Pro, and Team) with no Azure subscription required, plus dedicated Enterprise hosting in your own Azure subscription.
AI Rule Engine now supports MCP so AI assistants can initiate workflows, read files, inspect ruleset run logs, and operate with per-instance permissions.
Why human oversight matters in AI processes, and how AI Rule Engine connects AI providers, governed workflows, and MCP-enabled assistants in one controlled system.
Build XML payloads inside AI Rule Engine with a dedicated XML create action, similar to the existing JSON create action.
Save projects as reusable templates, start new projects faster, and publish templates to the marketplace similar to extensions.
Organizations now own projects, templates, and extensions, while environments move to the organization level so teams can share infrastructure and reduce cost.
Watch this tutorial to learn how to apply an AI Rule Engine extension inside your own project and reuse shared functionality faster.
Watch the AI Rule Engine demo video to see no-code rules, reusable workflows, and AI-powered automation in action.
Extend and share AI Rule Engine functionality without writing code.
A New Era of Intelligent Automation
Learn how to quickly install AI Rule Engine
Introductory video and post about AI Rule Engine