AI agents that enforce policy
The policy execution engine for workflows too complex for anyone else to solve. 4-5x more token efficient than ReAct agents.
What is MightyBot?
A policy-driven AI agent platform for regulated workflows.
MightyBot turns plain-English policies, messy documents, and system data into deterministic execution plans that process work, enforce rules, and produce audit-ready evidence across lending, insurance, payments, and compliance operations.
Explore the MightyBot platform, see how the policy engine works, or compare MightyBot against other AI agent platforms for regulated industries.
THE HARD TRUTH
The hardest workflows have resisted automation for decades. Until now.
Commercial loan underwriting. Insurance claim adjudication. Compliance monitoring. These workflows haven't been automated, not because the technology didn't exist, but because the architecture didn't.
Extracting data with high confidence
Applying layered business rules
Executing actions across multiple systems
Maintaining audit trails that satisfy regulators
The Paradigm
Systems of record → Systems of action
Your organization runs on systems of record—loan platforms, document repositories, compliance databases. They store what happened. They don't do anything about it.
MightyBot transforms them into systems of action.
Data flows in through the data engine. Policies compile into agent execution. Decisions, actions, and documentation move across enterprise integrations. The next item is processed continuously, autonomously, and without waiting for a human to click "approve."
The Capabilities
Every decision traceable. Every action defensible. Every audit passed.
The hardest workflows exist in regulated industries, where audit trails aren't optional, errors have legal consequences, and "good enough" gets you sued.
MightyBot was built for situations where mistakes are unacceptable and nuance matters.
This is what production-grade means in regulated industries: compliance controls, secure execution, and audit trails for autonomous agents.
Collaborative Policy Refinement
SMEs write and update policies in plain English. Every correction auto-improves the next execution. No dev sprints needed.
Managed Backtesting
Test policy changes against real historical data before they go live. See how a rule change affects thousands of past decisions.
Regulatory-Grade Audit Trails
Every decision traced to the policy, data, and timestamp that produced it. Generated automatically, not assembled after the fact.
The Behavioral Flywheel
Every correction compounds.
Every deployment gets smarter.
Full observability into every decision. Every action traceable. When your team spots something—an edge case, a policy nuance, a needed refinement—they flag it.
The agent absorbs that feedback and improves. Not in a future release. Not in next month's update. In the next execution.
A proprietary evaluation system measures accuracy, time saved, and human overrides. Teams refine policies. The agent improves. The advantage compounds.
MightyBot provides closed-loop improvement with immediate adaptation. The more it runs, the smarter it gets.
Policy to Production
Plain English policies. Deployed today. Not next quarter.
The business rules that govern your operations—the ones trapped in procedure manuals and tribal knowledge—become executable policies.
Written in plain English. Not code. Not flowcharts. Not six-month implementation projects.
Ship workflows as versioned definitions in Git. Engineers can review, modify, and deploy with standard tools.
Only MightyBot makes this possible for complex regulated workflows.
Customer Success
MightyBot's platform seamlessly integrated into our tech stack without any re-architecture. They plugged in as an AI exoskeleton for our existing platform, enhancing our capabilities without disruption. Together, their technology and our platform helped us bring this incredibly powerful product to market in weeks, not years, reducing time on task by nearly 95% and accelerating draws to borrowers by up to 60%.Thomas Schlegel, VP of Engineering at Built
AI Agent Platform FAQ
Questions regulated teams ask before deploying AI agents.
Short answers for compliance, operations, product, and engineering leaders evaluating production-grade AI agent platforms.
What is MightyBot?
MightyBot is a policy-driven AI agent platform for regulated workflows. It turns plain-English policies, messy documents, and system data into deterministic execution plans that process work, enforce rules, and produce audit-ready evidence.
How is MightyBot different from a generic AI agent platform?
Generic AI agent platforms help teams build agents that reason and act. MightyBot is built for regulated workflow execution: it compiles policies into deterministic plans, evaluates source evidence, executes actions across systems, and records audit trails for every decision.
How does MightyBot enforce policy?
Domain experts write policies in plain English. MightyBot compiles those policies into executable plans, applies them to documents and system data, routes exceptions to human review, and records the policy version and evidence behind each action.
Which regulated workflows can MightyBot automate?
MightyBot is designed for workflows such as commercial loan underwriting, construction draw reviews, insurance claims processing, payments operations, compliance monitoring, document processing, financial spreading, and credit risk evaluation.
How does MightyBot create audit trails?
MightyBot records the source documents, extracted data, policy version, decision logic, timestamps, human review events, and downstream actions that produced each outcome, so regulated teams can explain what happened without rebuilding evidence after the fact.
How is MightyBot different from RPA or ReAct agents?
RPA automates brittle user-interface steps, and ReAct agents reason through tasks at runtime. MightyBot compiles policies into deterministic execution plans before runtime, which makes regulated workflows more predictable, auditable, and cost-controlled.