Where rules live today
Procedure manuals. Tribal knowledge. Long-time employees. Your policies are scattered across systems and people.
PLATFORM
Policy Engine for AI Agents
Your policies become executable: enforced on every decision, versioned like code,
traced to source. No code. No flowcharts.
What is a Policy Engine for AI Agents?
A policy engine for AI agents is the control layer that turns business rules into executable decisions. MightyBot converts written policies into versioned logic that agents enforce across every transaction. This is plain-English policy automation: no code, no flowcharts, and every evaluation traced to its source. That is where the productivity comes from: decisions that finish correctly the first time.
Why Regulated AI Agents Need a Policy Engine
Every regulated organization has the same problem. The business rules that govern operations are trapped in procedure manuals, tribal knowledge, and the heads of people who have been there for twenty years.
What came before
Traditional rules engines forced compliance teams into brittle nested conditionals. Every policy update became an engineering project.
The core issue
The people who know the rules cannot update the rules directly. A translation layer sits between policy and execution.
Your Policies. Deterministic Execution.
Policies are written in plain English by your compliance officers, underwriters,
and operations managers.
That is the policy. The engine compiles it into deterministic evaluation logic.
Every application evaluated consistently. Every evaluation traceable to the source
rule.
The people who know the rules write the rules. The engine enforces them.
Written by the Business
Compliance officers, underwriters, and operations managers own the rules. Not engineers.
Compiled, Not Interpreted
The engine compiles plain English into evaluation logic that runs consistently on every transaction.
Consistent and Traceable
Every application evaluated consistently. Every evaluation traceable back to the policy that governed it.
Traditional rules engines vs policy engines for AI agents
Policy engines change how business rules are authored, applied, updated, and evidenced.
| Dimension | Traditional rules engine | Policy engine for AI agents |
|---|---|---|
| Authoring | Code and flowcharts | Plain English |
| Scope | Structured inputs | Documents and systems |
| Change process | Engineering release | Versioned policy update |
| Evidence | Log lines | Decision trace with source pointers |
Extensible Policy Library
MightyBot ships with pre-built policies covering regulatory compliance checks, credit risk thresholds, document completeness validation, covenant monitoring, underwriting guidelines, anti-fraud rules, and KYC / AML screening. These are battle-tested starting points, not rigid templates. Your team customizes them and creates new policies to match specific requirements. The library turns a months-long deployment into weeks.
Many financial institutions already run on these policies.
- Regulatory compliance checks Compliance
- Credit risk thresholds Risk
- Document completeness validation Validation
- Covenant monitoring Monitoring
- Underwriting guidelines Underwriting
- Anti-fraud rules Fraud
- KYC / AML screening Compliance
Policy Profiles: One Workflow, Every Regime
Regulated operations rarely run under a single rule set. Jurisdictions impose different obligations. Counterparties hold different documentation standards. Products carry different regulatory treatment. Most platforms force a choice: duplicate the workflow for each regime, or bury the differences in branching logic nobody can audit. Policy profiles keep one workflow and vary the rules where they must vary. Same workflow, different profiles, no duplication.
Every decision records the exact profile that governed it, and reruns execute against the original policy snapshot. When an auditor asks what rules applied, the answer is unambiguous.
- Baseline workflow policy, shared across all contexts Workflow
- Counterparty documentation standards Profile
- Jurisdiction disclosure obligations Profile
- Product-specific regulatory treatment Profile
Git-Native Versioning
Every policy change is version-controlled. Full history. Every version timestamped, attributed, and auditable.
When a compliance officer updates a policy, the previous version remains accessible. Audit teams see exactly which version was in effect when any decision was made. Policy updates deploy without disrupting in-flight transactions.
Ship workflows as versioned definitions in Git. Roll back in seconds. Audit across any time window.
The Policy Authoring Studio
Write rules in natural language. Attach validation criteria. Set enforcement behavior: hard block, soft warning, or flag for review. Each policy links to the data fields it evaluates.
01
Write in natural language. Attach validation criteria to each rule.
02
Set enforcement behavior: hard block, soft warning, or flag for review.
03
Test in sandbox against historical transactions. No risk to live workflows.
04
Deploy to production.
Your policies. Deterministic enforcement. 99%+ document extraction accuracy in a production lending deployment.
Estimate the value of policy-driven automation with the AI agent ROI calculator.
Request a demoExplore the stack
Where policies become execution.
Agent CompilerFrom plain English to working agent. No canvas, no flowcharts: describe the work and the platform compiles the execution plan.RuntimeCompiled Agent ExecutionParallel execution plans for regulated workflows instead of sequential prompt chains.EvidenceData EngineDocument intelligence, canonical extraction, and source evidence pointers.SystemsEnterprise IntegrationsConnect systems of record to systems of action without rip-and-replace.AuditCompliance InfrastructureWhy-trails, review gates, evidence records, and examiner-ready exports.SecuritySecure AI Agent PlatformSOC 2 Type II controls, tenant isolation, encryption, and access governance.Measured evidenceAgent Evaluation Cost StudyMeasured token costs of single-pass, agentic, and voting configurations on the same workload.
FAQ
Frequently Asked Questions
What is plain English policy automation?
It is the practice of writing business policy in ordinary language and having software compile it into executable rules. In MightyBot, a credit or operations team writes the policy as they would state it; the platform compiles it into versioned, testable logic that agents apply the same way every time.
How do AI agents enforce business rules?
Each transaction is evaluated against the active policy version. The engine records which rule fired, what evidence it used, and what outcome it produced, so enforcement is deterministic and reviewable rather than dependent on a model's judgment in the moment.
What is a policy engine for AI agents?
A policy engine for AI agents turns business rules into executable, versioned logic that agents enforce consistently. MightyBot lets compliance and operations teams write policies in plain English, test them in a sandbox, and deploy without code.
How does the engine handle conflicting rules?
Policies have defined precedence. When rules overlap, the engine applies the most restrictive applicable rule. The full evaluation chain is logged for audit.
Can I test policy changes before they affect production?
Yes. The sandbox runs policies against historical transactions so your team can validate behavior before anything goes live.
How does versioning work with active transactions?
In-flight transactions continue under their starting version. New transactions pick up the latest policy set. Both are preserved with full change history.
What types of policies does the library include?
Regulatory compliance, credit risk, document completeness, covenant monitoring, underwriting guidelines, anti-fraud, and KYC / AML across lending, insurance, and payments.
How does MightyBot handle different rules by state, counterparty, or product?
Policy profiles. The workflow defines the baseline logic; profiles vary specific rules by jurisdiction, counterparty, or product without duplicating the workflow. Every decision records the exact profile in effect, and reruns execute against the original policy snapshot.
Can a single policy enforce across multiple systems?
Yes. Policies enforce at the agent execution layer across connected systems. A single policy can evaluate document data, system-of-record fields, and API context in one pass.
Last updated: August 6, 2026