Introduction

The Problem

Operations, whether in manufacturing, financial compliance, or supply chain logistics, are built on strict rules, compliance, and predictability. They require determinism.

But modern AI architectures are fundamentally probabilistic. When organizations try to deploy autonomous agents into high-stakes operational environments, the system breaks down. LLMs hallucinate, multi-step execution plans drift from their original intent, and black-box reasoning makes it impossible to audit why a specific action was taken.

You cannot run scalable, autonomous operations if your execution layer is based on "vibes."

Currently, the industry tries to solve this by wrapping agents in brittle Python scripts or forcing task-oriented AI into existing human assembly-lines, requiring constant human-in-the-loop babysitting. The root problem remains: current AI architectures lack the controllability and determinism required to be trusted with production-grade business operations.

Our Solution is Simple

Rather than trying to force a probabilistic agent to act deterministically, we built an execution architecture that constrains AI to your exact business logic.

To get AI to work effectively in your sensitive, highly regulated workflows, you don't need a better prompt; you need an AI operating system that forces probabilistic reasoning to adhere to strict operational contracts and access controls.

We'll provide the tools to codify your operating model, and an AI operating system through which to execute it.

We're building the unified control plane for you to architect your autonomous operations at scale.

What is RARS?

We weren't convinced with the current approaches to AI-driven workflows. After evaluating concepts like agents, multi-agent systems, and observability and evaluation trends, we determined there were several major problem areas: trust, control, determinism, and verifiability.

So we started from scratch, and we rethought everything.

RARS is a symbiosis of the reasoning capabilities of probabilistic AI (eg. LLMs) and a symbolic programming runtime assembled from your codified operating model.

Basically, RARS is the world's most powerful coding agent, it just writes code against your business runtime.

Deterministic Orchestration & Execution

The fundamental problem with AI workflows is the ReAct loop: reason, act, observe, reason again. Every step is a fresh inference. Multi-step plans drift. The execution is as non-deterministic as the reasoning.

RARS introduces ProActive AI: your AI manipulates your business through a concept-oriented programming runtime where your domain objects have types, behaviors, and inheritance hierarchies, just like classes in OOP. Actions are methods on concepts. Type-based dispatch provides object-level overloads for semantically distinct resources. The symbolic engine executes workflows deterministically against this typed graph.

The plan runs as written, not re-inferred at each step. Within a single script, deterministic API calls, AI reasoning steps, and human approvals compose into one executable plan with aligned I/O contracts.

Every action, whether a deterministic service call or an agentic reasoning task, has an explicit I/O contract validated against your business constraints. An AI sub-agent's output is held to the same validation as a direct API response. This is Contractual AI: the contract guarantees the output structure regardless of how the result was produced. Continuous validation acts as compiler diagnostics for your business state, catching violations in real time.

CONSTRUCT {
    ?workOrder  wo:status      ?status ;
                wo:priority    ?priority ;
                wo:approvedBy  ?approver .
}
WHERE {
    # Read a workorder from the existing runtime state
    ?workOrder a wo:WorkOrder ;
               wo:workOrderId "WO-2024-0891" .

    # Invoke an agentic AI action to assess risk
    ?assessment wo:AssessRisk (?workOrder) .

    ?assessment wo:priority ?priority .

    # Pause for human approval
    ?approval wo:RequestApproval (
        ?workOrder
        wo:assessment ?assessment
    ) .
    ?approval wo:approvedBy ?approver .

    # Mutate an external system
    ?dispatch wo:DispatchWorkOrder (
        ?workOrder
        wo:approval ?approval
        wo:priority ?priority
    ) .

    # Select the updated status
    ?workOrder wo:status ?status .
}

Learn more: Contractual AI | The NeuroSymbolic Engine | The Semantic Operating System | Designing Actions

Verifiable

If you look at where AI has had the biggest impact, it's by far been in software engineering, specifically coding.

You might think this is because software engineers are already in the space, are more likely to use the tools early, or one of many other reasons, but we have a different take: it's because software has version control.

Brandolini's law: The amount of energy needed to refute bullsh*t is an order of magnitude bigger than that needed to produce it.

When you use AI to code, do you evaluate the output by going through the traces and reasoning processes of the AI every step of the way? Or do you just look at the GIT diff?

At Poliglot, we tend to just look at the PR showing the exact lines of code that were changed, and make a decision of whether if it solves our problem.

RARS exposes a GIT diff for your business objects. It's stateful execution layer provides a staging-ground for multi-step workflows that turns changes to your operating resources (contracts, project management issues, budgets, accounting statements, etc.) into a structured diff that can be reviewed, committed, or iterated on.

  po:PO-2024-0891 a po:Procurement ;
-      po:status po:PendingReview ;
+      po:status po:Approved ;
+      po:approvedBy <urn:users:123> ;
+      po:approvedAt "2025-03-28T14:22:03Z" ;
+      po:value "340000.00"^^xsd:decimal .

No more reviewing full traces, evals, and reasoning chains, but, our native observability system still provides it if you really need it.

Learn more: Persistent Memory | Provenance

Trustworthy

When multiple agents, workflows, and human operators all contribute to the same business state, trust requires more than logs. It requires an aligned world view.

RARS aligns every actor and every action around a single, collaborative representation of your operational state. Everything is a structured observation: an attestation made by a specific agent, as part of a specific process. When an autonomous actor updates a record, a service syncs external data, or a human approves a change, they're all contributing observations to the same shared state. No actor operates in isolation. No data exists without attribution.

This shared world view is what makes collaboration between humans, AI, and automated systems actually work. Everyone sees the same state. Everyone's contributions are structured the same way. And every change, down to the individual field, carries a complete chain of attribution: who, why, when, and as part of what process. That's not a forensic investigation. It's a direct lookup:

SELECT ?actor ?process ?origin WHERE {
  ?obs a rars-os:Observation ;
       rars-os:attests << po:PO-2024-0891 po:status po:Approved >> .
       rars-os:accordingTo ?actor ;
       rars-os:recordedIn ?process .

  ?process rars-os:authContext/rars-iam:origin ?origin .
}

Learn more: The Collaborative Runtime | Provenance

Controllable

Static role-based access control is dangerous for AI. A role grants permissions unconditionally, regardless of whether the business context supports the action. An AI with "approver" access can approve anything its role allows, whether or not a risk assessment was completed, a budget was reviewed, or the request makes any sense at all.

RARS enforces situational access control: permissions that evaluate the live state of the business at the moment of execution. Authorization policies carry conditions that are evaluated against your operational state in real time. Not just "does this role have permission," but "does the current situation support this action."

For example: an autonomous actor tries to dispatch a $250,000 electrical work order. At execution time, the system evaluates: has the risk assessment been completed? Has a licensed electrical engineer signed off? Is the project budget sufficient to cover the cost? Has a manager with the appropriate authority approved it? If any condition isn't met, the operation is denied, regardless of the agent's role.

You grant broad operational capability while maintaining precise control over when that capability can and should be exercised.

Here's what a situational access policy looks like:

po:ProcurementManagerPolicy
  a rars-iam:IdentityPolicy ;
  rars-iam:effect rars-iam:Allow ;
  rars-iam:action rars-act:InvokeAction ;
  rars-iam:resource po:ApproveProcurement ;

  # direct invocation only
  rars-iam:condition [
    rars-iam:scope rars-iam:AuthorizationContext ;
    rars-iam:onProperty rars-iam:delegationDepth ;
    rars-iam:hasValue 1
  ] ;

  # ops prerequisites on the subject
  rars-iam:condition [
    rars-iam:scope rars-os:Process ;
    rars-iam:sparql [
      rars-os:ask """
        ASK {
          ?scope rars-os:parent ?inv .
          ?inv rars-act:subject ?order .

          ?order po:riskAssessment/po:status po:Completed .
          ?order po:engineerSignOff/po:status po:Completed .
          ?order po:value ?v .
          FILTER(?v <= 500000)
        }
      """
    ]
  ] .

When a script attempts to invoke the protected action:

SELECT ?result WHERE {
  # sync latest procurement details
  ?proc po:GetProcurement(
    po:id "PO-2024-0891"
  ) .

  # try to approve the procurement
  ?result po:ApproveProcurement (?proc) .
}

The runtime evaluates all policy conditions against the live business state:

Learn more: Situational Access Control | The Identity Model | Security

Key Concepts

Before diving in, here are the foundational concepts you'll see throughout the documentation:

• Workspace: an organizational environment where matrices are installed and your team collaborates.
• Context: a persistent working environment where RARS operates. Like a business context but in the digital world: you have information in front of you, you know which systems to use, you remember what happened before, and you pick up where you left off.
• Matrix: a codified operating model for a business domain. Defines the domain's concepts, rules, operations, and systems of record. Versioned, composable, installable.
• Action: a declared operation RARS can execute. Can be a deterministic API call, an orchestrated multi-step workflow, an AI reasoning task, or a human approval. All share the same verifiable I/O contract.
• Observation: every piece of data from your operations state, and every change RARS makes is recorded as an observation with full provenance: who, what, when, and why, and as part of which process. Your entire business becomes auditable through a unified system.

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