Superagent

Superagent is an AI safety platform backed by Y Combinator that helps companies protect their AI applications from prompt injections, data leaks, and compliance failures while providing customer-facing proof of security measures. While most AI development focuses on capability improvements, Superagent addresses the critical but often overlooked challenge of making AI systems defensible to enterprises, regulators, and security-conscious customers. The platform embeds small language models as runtime guardrails that block attacks and prevent data exposure in real-time, runs adversarial safety tests to identify vulnerabilities before deployment, and generates a public-facing Safety Page that demonstrates security controls to prospects and procurement teams. This integrated approach recognizes that AI safety requires both technical controls and transparent communication, especially for startups selling to enterprises where security failures can end contracts or delay sales cycles. Superagent is used by companies including Capchase, SAP, and Bilanc, demonstrating traction in environments where AI safety directly impacts revenue and customer trust. The platform's focus on provable safety rather than just claiming security aligns with increasing regulatory scrutiny and enterprise demand for defensible AI systems.

How It Works

Superagent operates through three integrated components that provide runtime protection, proactive testing, and transparent proof of safety measures. The Safety Agent embeds directly into AI applications as a protective layer, analyzing inputs and outputs in real-time using specialized small language models optimized for security detection rather than general reasoning. When users submit prompts, the Guard component screens for injection attempts and jailbreak patterns, blocking malicious inputs before they reach the primary AI model. The Redact component inspects AI-generated outputs, identifying and removing sensitive data like personal information, credentials, or proprietary details before responses reach users. The Analyze component reviews files for compliance issues when AI systems process documents. Beyond runtime protection, Safety Tests continuously probe the system with adversarial examples designed to trigger vulnerabilities, simulating attacker techniques and identifying weaknesses before real exploitation occurs. These tests generate evidence logs documenting system behavior under attack, satisfying compliance requirements for security testing. The Safety Page component transforms test results and runtime statistics into a public-facing dashboard that organizations share with prospects during sales cycles, providing immediate proof of security measures even while verification processes continue. This three-layer approach combines prevention, detection, and communication to address both technical security and business requirements.

Core Features

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  Safety Agent Runtime Protection embeds small language models directly into AI applications to provide real-time defense against attacks and data exposure. The Guard component blocks prompt injections and jailbreak attempts that try to manipulate AI behavior, the Redact component prevents sensitive data leaks by sanitizing outputs before they reach users, and the Analyze component inspects files for compliance issues when AI systems process documents. This runtime layer operates continuously without degrading application performance or user experience.

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  Adversarial Safety Tests proactively identify vulnerabilities before attackers exploit them through systematic testing with malicious inputs. The platform generates attack scenarios including prompt injection variations, data extraction attempts, and edge cases that trigger failures, executes these tests against the protected AI system, documents results with detailed evidence logs, and provides remediation guidance for discovered weaknesses. This testing produces the compliance evidence enterprises require during procurement.

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  Customer-Facing Safety Page creates a public-facing dashboard displaying security controls and test results that organizations share with prospects and procurement teams. The Safety Page shows implemented guardrails, recent test coverage and results, compliance certifications in progress, and real-time security metrics. Organizations can share this page immediately even while waiting for formal verification, accelerating sales cycles by providing transparent proof of safety commitment and reducing back-and-forth security questionnaires.

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  Usage-Based Pricing Model aligns costs with actual utilization rather than fixed subscriptions, making enterprise-grade safety accessible to startups and growing companies. Organizations pay based on the volume of protected requests and tests executed, scaling costs with product adoption rather than paying for unused capacity. This model reduces the barrier to implementing comprehensive AI safety for companies uncertain about usage patterns or in early growth stages.

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  Compliance Evidence Generation automatically produces the documentation required to satisfy enterprise procurement and regulatory requirements. The platform logs all safety tests, attack attempts, and system responses with detailed timestamps and context, generates reports demonstrating security testing coverage, tracks remediation of discovered vulnerabilities, and provides audit trails proving continuous security monitoring. This automated evidence generation saves engineering time and enables confident responses to security questionnaires.

Who This Is For

Superagent serves companies building AI agents and applications for enterprise customers, regulated industries, or security-conscious markets where safety failures carry high consequences. Startups selling to enterprises benefit from the Safety Page that provides immediate proof of security commitment during sales cycles, addressing procurement objections before they derail deals. AI teams in regulated industries like healthcare, finance, or government use Superagent to satisfy compliance requirements for adversarial testing, data protection, and audit trails without building custom security infrastructure. Product teams needing to demonstrate defensible AI safety to investors, customers, or regulators leverage the platform's automated evidence generation and transparent reporting. Engineering organizations wanting to prevent catastrophic failures such as data leaks that damage reputation or breach contracts implement Superagent's runtime guardrails as insurance against unexpected AI behavior. The platform particularly suits teams where security delays sales or requires significant engineering effort to prove compliance, and where the cost of a single data leak or prompt injection incident exceeds the platform investment. However, internal tools with minimal security requirements or applications serving non-sensitive use cases may find Superagent's safety focus excessive for their risk profile.