Pegasi

Product Introduction

1. Definition: Pegasi is a runtime control layer for enterprise AI agents, specifically designed for regulated industries. It is a software platform that sits between AI agents (like those built on Claude or OpenAI) and their target applications, intercepting and governing actions in real-time.
2. Core Value Proposition: Pegasi exists to enable regulated enterprises to safely automate high-stakes, sensitive workflows with AI agents without sacrificing control, compliance, or auditability. Its core value is providing a governance framework that includes human-in-the-loop approvals, dynamic policy enforcement, full observability, and immutable audit trails for AI agent operations.

Main Features

1. Approval Gates & Human-in-the-Loop Controls: This feature allows operators to set pre-execution intercept points for sensitive agent actions. Before an agent executes a step like updating a card-on-file or submitting a vendor packet, the workflow can be paused and routed to a human for review and approval within a decision queue. This ensures critical decisions retain human oversight.
2. Policy-Based Runtime Enforcement: Pegasi enables the creation and enforcement of granular security and compliance policies that are applied at the agent's runtime. For example, a policy like card_on_file.v2 can block an update request if the user session isn't verified, forcing a retry. Policies act as automated guardrails that steer agent behavior without manual intervention.
3. Comprehensive Audit Trail & Observability: Every agent run is logged with full context, creating an audit-ready evidence trail. The platform provides session activity logs detailing each action (e.g., "SFDC open acct", "Slack send summary"), linked to the specific actor, policy, and UTC timestamp. This meets strict regulatory requirements for traceability in processes like financial compliance reporting or claims processing.
4. Workflow Recorder & Continuous Learning: Pegasi includes tools to capture and codify human workflows. Users can record a process (like vendor onboarding) once in a browser, creating reusable, step-by-step agent instructions. The "Review" phase of each run allows for corrections, which feed into a continuous learning loop to improve agent accuracy and reliability over time until they earn auto-approval.
5. Enterprise-Grade Security & Deployment Flexibility: The platform is built with enterprise procurement in mind, offering SOC 2 Type I certification, HIPAA compliance design, and a commitment that customer data never trains models. It can be deployed as a hosted cloud service or on-premise within a company's own private environment for maximum data control.

Problems Solved

1. Pain Point: The inability to trust AI agents with sensitive, regulated business operations due to risks of errors, lack of oversight, and non-compliance with audit requirements. Manual review of all AI outputs is slow and unscalable, while ungoverned automation is too risky.
2. Target Audience: This includes Compliance Officers and Risk Managers in finance, healthcare, and insurance; Operations Leads and IT Security teams in FinTech and Enterprise Technology; and Back-Office Managers overseeing processes like vendor onboarding, payroll reconciliation, and KYC verification.
3. Use Cases: The product is essential for automating card-on-file updates across third-party portals, payslip extraction and verification for audits, insurance claims processing, vendor onboarding with document collection, compliance report generation, KYC checks, and internal audit checks where a verifiable log of actions is mandatory.

Unique Advantages

1. Differentiation: Unlike simple AI agent orchestration frameworks, Pegasi is not about building agents but about controlling them post-deployment. It differs from manual oversight by providing structured, policy-driven intercepts rather than ad-hoc reviews. Compared to building custom governance, it offers a pre-built, certified platform.
2. Key Innovation: Pegasi's runtime control layer is a distinct architectural innovation. It decouples the agent's reasoning from its permission to act, applying security and compliance checks at the moment of execution. This is powered by its research-backed engine for hallucination detection and compliance scoring (e.g., FRED for financial hallucination detection), making its controls intelligent and context-aware.

Frequently Asked Questions (FAQ)

1. What is an AI agent runtime control layer? An AI agent runtime control layer is a software platform that governs the actions of autonomous AI agents in real-time as they execute tasks. It enforces policies, requires human approvals for sensitive steps, and logs all activity to ensure safety, compliance, and auditability in enterprise environments.
2. How does Pegasi ensure compliance for regulated workflows? Pegasi ensures compliance by embedding approvals and policy enforcement directly into automated workflows, providing a complete, immutable audit trail for every action, and offering certifications like SOC 2 and HIPAA-ready design. It turns AI agent operations into a documented, reviewable process that meets regulator expectations.
3. Can Pegasi work with any AI agent or model? Pegasi is designed to be model-agnostic and can interface with various AI agents and foundational models (like Claude or OpenAI). Its control layer operates at the action level, meaning it can govern the steps an agent takes (clicks, API calls, data entries) regardless of the underlying language model generating the plan.
4. What is the difference between hosted and on-premise deployment? The hosted deployment runs Pegasi in Pegasi AI's cloud environment (SOC 2 certified), while the on-premise deployment allows a company to install and run the identical Pegasi software within its own private data center or VPC, ensuring full data isolation and control for the most stringent security requirements.
5. How does the continuous learning feature work? When a human reviewer corrects or approves an agent's action during the "Review" phase, that feedback is used to improve the system. Over time, this reduces the need for manual intervention for specific, well-understood steps, allowing the agent to progress toward auto-approval for those tasks while maintaining safety.