PLATFORM

AI Agent Compliance Infrastructure

MightyBot generates the compliance record as agents work: decision traces, audit trails, review gates, and exportable evidence. Analysts review it; they never have to rebuild it.

What is AI Agent Compliance Infrastructure?

AI agent compliance infrastructure must prove what an agent did, why it did it, and which policy and data controlled the outcome. MightyBot generates that proof as agents work: decision traces, regulatory-grade audit trails, human review gates, and exportable compliance records.

What Regulators Need to See From AI Agents

Audit trails are not optional. Every decision must be reconstructable. Every data point traceable. Every policy version documented.

"The AI decided" is not an acceptable answer to a regulator. They need to know which rule, what data, where it came from, and which policy version was in effect.

Most AI platforms log that a decision was made. MightyBot logs how and why, with evidence links that survive regulatory examination.

Regulated teams in commercial lending, insurance, and financial services can use the same evidence model.

What AI Agent Compliance Infrastructure Must Capture

Capability
What It Means
Decision traces
Every outcome linked to policy, data, and timestamp
Regulatory-grade audit trails
Generated automatically, not assembled after the fact
Human review gates
Approve, override, or escalate at the points that matter
Stateful workflows
Pause, continue, resume without breaking compliance
Multi-tenant isolation
Your data and policies are yours alone
Git-native versioning
Full history of every policy and workflow change

Every row is production infrastructure. Shipping today.

What regulators and auditors map this to

These capabilities map execution evidence to the frameworks regulators and auditors use to evaluate controlled AI systems.

  • SOC 2 Type II: decision traces and access controls fall under the AICPA Trust Services Criteria that SOC 2 reports audit.
  • Model risk management (SR 11-7): versioned policies and challenge evidence support the Federal Reserve's model risk guidance.
  • EU AI Act: logging, human oversight, and traceability obligations for high-risk AI systems are addressed by the EU AI Act framework.
  • GDPR: data-handling records and processing evidence align with GDPR requirements.
  • ISO/IEC 27001: information-security management alignment.

AI agent audit trails

Every decision generates a complete trace: policy version, data values checked, evidence pointers to source documents, evaluation results, timestamps, final determination. Inside MightyBot it is called the why-trail. Not just what happened, but why.

When an auditor asks why a loan was approved, the trace produces the answer in seconds: decision to policy to extracted value to the pixel on the source document.

Human Review Gates

  1. Review Gates

    Define where human approval is required. The workflow pauses, presents the trace, and resumes after approval.

  2. Management by Exception

    Clean applications proceed automatically. Edge cases route to reviewers with full context. Your team reviews the 5% that matter.

  3. Escalation Policies

    Who reviews what, under which conditions. Managed in the Policy Authoring Studio.

Autonomy Is Earned, Not Assumed

Mode
What It Means
Audit
Agents run the full workflow; your team keeps making the decisions. Output quality is measured against what reviewers actually do.
Assist
Agents prepare decisions and evidence; reviewers approve, edit, or override at the gates.
Automate
Clean cases execute end to end. Exceptions still route to humans with full context.

The platform measures how much reviewers change agent output. When edits approach zero, you have the evidence to raise autonomy. When they do not, you know exactly where the gaps are.

Git-Native Policy Versioning

Every policy change creates a new version. Timestamped. Attributed. Active transactions continue under their starting version. New transactions use the current version. Workflow definitions versioned the same way.

Ship workflows as versioned definitions in Git.

Git-Native Versioning diagram

Compliance Exports

S3 export Snowflake export Iceberg export

All exports checksummed and logged. Failed exports trigger alerts and retries.

See regulatory-grade compliance for autonomous agents.

Request a demo See the Architecture

Explore the stack

The rest of the Decision Execution Stack.

Agent CompilerFrom plain English to working agent. No canvas, no flowcharts: describe the work and the platform compiles the execution plan.ControlPolicy EnginePlain-English policies compiled into deterministic execution paths.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.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 makes an AI agent compliance platform different from a GRC tool?

A GRC tool documents controls after the fact. An AI agent compliance platform generates the evidence at execution time: every agent decision lands with its policy version, inputs, and evidence pointers attached, so the compliance record is a byproduct of the work rather than a quarterly assembly project.

How do you keep AI workflows compliant in regulated industries?

Write the policy as the control, execute it deterministically, gate the exceptions to humans, and keep a replayable trail. Examiners get the same view the operations team had: what ran, under which policy version, on what evidence, with which overrides.

What does an AI agent audit trail capture for each decision?

Policy version, data values, evidence pointers to source documents, evaluation results, timestamps, final determination, and human review actions. Every element is linked and reconstructable.

Can regulators access the audit trail directly?

Compliance teams generate reports and export decision records via S3, Snowflake, and Iceberg. Direct access level depends on your organization's preferences.

How does MightyBot handle policy changes mid-quarter?

In-flight transactions continue under their active version. New transactions pick up updates. Both preserved. No ambiguity.

Can we configure different review thresholds for different transaction types?

Yes. Review gates and escalation policies are configurable per workflow, transaction type, dollar amount, risk level, or any policy-defined criteria.

Does MightyBot support data retention policies?

Yes. Retention configurable per workflow and data type. Automated archival or deletion when periods expire. Retention policies themselves versioned and auditable.

How is compliance export data integrity ensured?

Checksummed and logged. Failed exports trigger alerts and retries. Schema validation ensures data matches your target system.

Last updated: August 6, 2026