Superagent.sh (Developer Platform)

Superagent.sh is an AI safety infrastructure platform designed to help teams ship AI agents and LLM applications that are robust against prompt injection, data leakage, and other adversarial behaviors. It positions itself as runtime defense and red‑team testing for AI systems, sitting between your applications, tools, and models to inspect prompts, tool calls, and responses in real time. Instead of focusing on building or hosting agents themselves, Superagent.sh focuses on hardening the AI systems you already run. The product offers adversarial testing where it actively attacks your production or pre‑production AI endpoints to surface issues such as data leaks, harmful or policy‑violating outputs, unsafe tool executions, and other unwanted actions before they reach real users. The team also provides open‑source tools, adversarial testing frameworks, and Safety Pages so organizations can document and demonstrate how their AI systems behave under stress. This makes it particularly relevant for companies deploying autonomous or tool‑using agents where a single compromised prompt or tool call can have outsized impact. In practice, developers and security teams integrate Superagent.sh with their existing AI stacks—LLM backends, orchestration frameworks, and custom agents—and configure test scenarios that mimic realistic attacks and misuse. Superagent.sh then monitors and analyzes inputs and outputs, flagging prompt injection attempts, jailbreaks, and suspicious tool usage via reasoning‑driven threat detection. The platform is built to continuously evolve as new attack patterns emerge, helping teams maintain an up‑to‑date safety posture across rapidly changing AI applications. What makes Superagent.sh distinctive is its focus on AI agent runtime defense rather than generic content filtering. According to its materials, it targets the specific failure modes of agentic and tool‑calling systems—like malicious tool calls, chained prompt attacks, and data exfiltration—rather than only screening final text outputs. Combined with its emphasis on adversarial testing, documentation, and safety infrastructure, it is aimed at organizations that already have sophisticated AI products but need security, governance, and compliance layers to deploy them with confidence.

• ✓Run continuous red‑team attacks against production AI agents to uncover data leaks, harmful outputs, and unsafe tool actions before users encounter them
• ✓Insert a secure proxy between your app and LLM to inspect, filter, and log all prompts and responses in real time
• ✓Validate and constrain tool and code execution calls from autonomous agents to prevent dangerous operations in production
• ✓Centralize observability, traces, and audit logs for AI traffic so security and engineering teams can investigate incidents and enforce policies
• ✓Scan repositories and agent configurations for prompt‑injection vulnerabilities and unsafe patterns as part of CI/CD

Start by creating an account and setting up a workspace, then deploy Superagent as a proxy layer between your application and LLM/provider so all prompts, tool calls, and responses flow through it. Configure safety policies for prompt injection detection, secret and PII redaction, and tool‑execution safeguards, using the dashboard to tune thresholds and rules. Integrate the open‑source SDK where appropriate to instrument your services, scan repositories, and run automated red‑team scenarios against your agents. Use the observability features—traces, audit logs, and policy views—to monitor how your agents behave in production and iteratively tighten defenses based on surfaced incidents. When testing or updating agents, run targeted red‑team campaigns via Superagent to proactively uncover data leaks or unsafe behaviors before deploying changes broadly.

As a runtime proxy, Superagent must be integrated into the critical path of your AI system, which can add architectural complexity and potential latency overhead. Its effectiveness depends on correct configuration of policies and routing of all relevant prompts and tool calls through the proxy; any bypassed paths may remain unprotected. Public documentation does not expose granular pricing or rate limits, making cost planning less transparent. The platform focuses on LLM and agent safety rather than model training or fine‑tuning, so teams still need separate solutions for model development and broader application security. Detection quality, while strengthened by a dedicated safety model, can still be subject to false positives or misses against novel or highly targeted attacks, requiring ongoing tuning and monitoring.