Formal Reasoning Infrastructure

Reasoning You Can Verify

MRS delivers mathematical proof of AI decision compliance - formal logic verification plus complete audit trails.

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verification_engine
?- verify_decision(agent_action, compliance_check).
  [OK] Codex Law [CL-003]: Authorization verified
  [OK] Z3 Constraint: Budget limit satisfied  
  [OK] Audit Trail: Complete provenance logged
Decision: APPROVED

Watch MRS In Action

5-minute theatrical demo: Three scenarios, 17 compliance checks, violations caught in real-time

This demo shows MRS evaluating agent actions against DFARS, SOX, and NERC compliance rules. Watch violations get caught with specific regulatory citations before execution.

5 minutes 17 compliance checks 3 scenarios
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Mathematical Proofs, Not Prompts

Most AI governance today is prompt-based - you tell the model to be careful and hope it listens. MRS uses formal verification.

Provably Correct

Z3 theorem prover generates mathematical proofs that decisions comply with constraints. Not heuristics—mathematical certainty.

Deterministic

The agent literally cannot take an action that violates the rules. Not a suggestion - a gate.

Testable

Run pytest against your compliance modules. 6/6 integration tests passing with measured performance under 11ms.

"Sub-millisecond formal proofs. That's what you hand to regulators."

Autonomous Agents Make Decisions. Can You Prove They Were Right?

The Opacity Problem

  • Black-box AI makes decisions
  • No way to verify reasoning
  • Compliance risk unknown
  • Trust requires faith, not proof

The Accountability Gap

  • Agent fails - who is responsible?
  • No audit trail for decisions
  • Cannot reproduce reasoning
  • Regulatory exposure

The Sovereignty Trap

  • Commercial platforms own your data
  • Vendor lock-in on critical systems
  • No control over reasoning logic
  • Dependency on extractive platforms

MRS solves all three.

Mirror Reasoning Stack: Formal Governance for AI Agents

Not a chatbot wrapper. Not a prompt filter. A formal reasoning substrate.

Prolog Rule Engine

Rules are formal logic, not English prompts

  • Evaluates every action against loaded compliance modules
  • DFARS, SOX, NERC, or custom rules written in Prolog
  • Violations caught with specific regulatory citations

Tamper-Evident Audit Trail

Every decision logged and exportable

  • Timestamped JSON logs suitable for regulator review
  • Complete provenance: which rule, what decision, why
  • No black box - compliance officers can verify the logic

Learning Loop

Agents that get smarter from their mistakes

  • Pattern detector identifies recurring failures
  • Belief manager creates versioned beliefs (never deletes)
  • Learned rules auto-injected into agent prompts

Sovereign Deployment

Your reasoning engine, your hardware

  • SWI-Prolog + Python + SQLite - no cloud dependencies
  • Runs on a Raspberry Pi or classified enclave
  • You own the rules, the logs, and the reasoning

Decision Verification Flow 

Agent proposes action
    |
MRSBridge (Python)
    |
Prolog Rule Engine
    |
APPROVED / VIOLATION DETECTED
    |
Audit Trail (JSON)
    |
Pattern Detection
    |
Belief Manager
    |
Context injected back into agent

Built For Regulated Industries

Healthcare

Clinical AI governance with complete auditability

  • Mathematical proof of AI decision compliance
  • Formal logic verification for clinical AI systems
  • Complete audit trails for every clinical recommendation

Government & Defense

Auditable AI for mission-critical operations

  • Auditable AI for contract compliance
  • ITAR/EAR reasoning verification
  • Federal acquisition rule enforcement
  • Complete decision provenance for oversight

Financial Services

Regulatory compliance automation with proof

  • Regulatory compliance automation
  • Risk decision verification
  • Audit-ready reasoning logs
  • Fiduciary duty enforcement

Critical Infrastructure

Safety-critical autonomous operations

  • Safety-critical autonomous operations
  • Verified decision-making under constraints
  • Incident response with full auditability
  • Zero-trust agent coordination

How It Works

Production-grade verification infrastructure

MRSBridge

Production

Python to Prolog interface

  • → assert_fact(), query(), load_module()
  • → export_audit_trail() for compliance export
  • → Tested: 6/6 integration tests passing

Z3 Formal Verifier

Production

Mathematical proof engine via SMT solving

  • → Sub-millisecond SAT/UNSAT proofs (0.14ms measured)
  • → Budget compliance and constraint satisfaction axioms
  • → Tamper-evident proof artifacts with SHA-256 hashes

Compliance Modules

Production

Pre-built regulatory rule sets

  • → Healthcare: Clinical AI disclosure, decision verification, audit trails
  • → Defense: DFARS, ITAR, budget authorization
  • → Financial: SOX, position limits, fiduciary duty
  • → Infrastructure: NERC CIP, cascade prevention

Pattern Detector

Beta

Learns from recurring failures

  • → Detects failure patterns from outcome history
  • → Triggers reflection engine for belief updates
  • → Tracks resolution effectiveness

Belief Manager

Beta

Versioned belief ledger

  • → Never deletes - only supersedes old beliefs
  • → Full provenance for every belief change
  • → Auto-injected into agent system prompts

DatalogBridge

Beta

Verified fact storage with provenance

  • → SQLite with MPPT tree traversal
  • → CSV + DuckDB-ready exports
  • → Session tracking and branch links

Integration Example

API Preview - Python SDK in development

python
from strikaris_mrs import MRSClient, CodexLaw

# Define your compliance rules
codex = CodexLaw()
codex.add_rule("budget_limit", "expenditure(X) :- X <= 10000")
codex.add_rule("authorization", "action(A) :- has_permission(agent, A)")

# Initialize MRS client with verification tier
mrs = MRSClient(
    codex_laws=codex,
    audit_enabled=True,
    verification_level="strict"  # Fast (2.56ms) or Strict (10.24ms)
)

# Agent proposes action
decision = mrs.verify_action(
    agent_id="procurement_agent",
    action="purchase_equipment",
    amount=8500,
    justification="critical_need"
)

if decision.verified:
    # Action approved with mathematical proof
    result = execute_action(decision)
    
    # Outcome tracked automatically
    mrs.log_outcome(decision.id, result)
else:
    # Violation detected before execution
    print(f"Denied: {decision.violation_reason}")
    print(f"Proof: {decision.z3_proof}")
    print(f"Evidence: {decision.audit_trail}")

Why MRS vs. Everything Else

CapabilityMRSPrompt EngineeringLLM Safety LayersCommercial AI Platforms
Rule enforcement [✓] Prolog formal logic English instructions Output filtering Black box
Violations caught [✓] Before execution Hopes agent complies After generation Unknown
Audit trail [✓] Every decision logged [X] Partial Platform-controlled
Regulatory citation [✓] Specific rule + reason [X] [X] [X]
Testability [✓] pytest on rules Manual review Manual review [X]
Deployment [✓] Your infrastructure Your LLM provider Your LLM provider Vendor lock-in

Most AI governance today is prompt-based - you tell the model to be careful and hope it listens. MRS is different. The rules are in Prolog. The agent literally cannot take an action that violates them.

Enterprise Infrastructure Pricing

Professional

For AI teams building compliant systems

$1,500 /month
  • Up to 5 agents
  • Hosted MRS instance
  • Standard Codex law library
  • Email + ticket support
  • Audit export (JSON/CSV/PDF)
  • 99.5% SLA
  • Setup fee: $2,500
Get Started
Most Popular

Enterprise

For organizations with mission-critical AI compliance

$5,000 /month
  • Unlimited agents
  • Dedicated MRS cluster
  • Custom Codex law development
  • Priority support + Slack
  • Compliance dashboard + reporting
  • TRAIGA/HIPAA/SOX audit packages
  • 99.9% SLA
  • Setup fee: $7,500
Request Demo

Sovereign

Defense, federal, and critical infrastructure

Starting $25K setup + $5K/month
  • Self-hosted; air-gapped deployment
  • Complete infrastructure control
  • Architecture consultation (Khan)
  • Custom verification protocols
  • DFARS/ITAR-grade security
  • White-glove onboarding + training
  • Dedicated support line
  • Annual compliance review
Contact Sales
Government contracts and federal agencies: contact for GSA Schedule and compliance documentation

Built on Proven Principles

Federal Contracting Ready

SAM.gov registered, CAGE coded, FEMA certified

Sovereignty-First

Local-first architecture, no platform dependency

Open Verification

Audit trails exportable in standard formats

Extensible Laws

Codex rules written in human-readable Prolog

Research-Validated

Active development since 2025, Z3 verification validated with measured performance

Technical Questions

RESTful API with SDKs for LangChain, AutoGPT, CrewAI, and custom implementations. Most integrations take 2-4 hours.
The action is blocked before execution. The agent receives the violation reason and audit trail. This prevents compliance failures, not just logs them.
Yes. Codex laws are written in Prolog (human-readable logic). We provide templates for common regulations and help you write custom rules.
Safety layers filter outputs. MRS verifies reasoning logic before execution. One prevents bad text; the other prevents bad decisions.
Verification adds 2-11ms per decision (measured: 2.56ms Prolog-only, 10.24ms with Z3 formal proofs). Z3 proofs complete in 0.14-40ms. For most enterprise use cases, this is negligible compared to the compliance risk reduction.
Yes. All decisions are logged with cryptographic hashing. Audit trails can be exported and verified independently.
Yes. Self-hosted deployment supports complete air-gap operation for defense/classified environments.
Z3 is a theorem prover from Microsoft Research that provides mathematical proofs of constraint satisfaction. MRS uses Z3 to prove compliance decisions are correct—not just check them, but mathematically prove them. Proofs complete in 0.14-40ms and generate tamper-evident artifacts.

Request a Live Demo

See MRS evaluate your specific compliance requirements. We'll walk through your regulatory framework and demonstrate real-time governance.

Ready to Make AI Decisions Verifiable?

Join government agencies, financial institutions, and enterprises building trustworthy autonomous systems with MRS.

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