Trent AI provides a continuous security platform for AI agents and autonomous workflows, automatically scanning code, dependencies, and runtime behavior to detect threats like prompt injection and data exfiltration. The system classifies risks, prioritizes remediation, and generates automated fixes through pull requests or configuration updates, integrating with popular AI IDEs and CI/CD pipelines.
Funding
$13M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
ACIDOSFounders
Product
Problem
Developers of AI agents and autonomous workflows lack security tools that understand and protect dynamic, prompt‑driven behavior, tool permissions, and data flows. Traditional scanners and periodic pen‑tests cannot keep up with continuously evolving agent code and runtime interactions, leaving critical risks such as prompt injection, tool misuse, and data exfiltration undetected.
Solution
Trent AI offers a unified, agentic security platform that continuously monitors, analyzes, and mitigates risks across the entire lifecycle of AI agents. Specialized security agents operate in a closed loop: a scanning agent observes code, infrastructure, and runtime behavior; a judging agent classifies findings, assesses business impact, and prioritizes remediation; a mitigation agent automatically generates fixes, opens pull requests, or adjusts configurations; and an evaluation agent tracks risk trends and forecasts future vulnerabilities. Each cycle feeds intelligence back into the loop, improving detection accuracy and remediation effectiveness over time. The platform integrates with common development environments (e.g., Claude Code, Lovable, OpenClaw) and CI/CD pipelines, delivering actionable remediation plans and automated fixes directly within developers’ workflows.
Target Audience
Primary customers are development and security teams building AI agents, autonomous workflows, and AI‑native applications—particularly those using platforms such as Claude, Lovable, OpenClaw, or custom agentic stacks.
Features
- Continuous scanning of agent code, dependencies, API calls, and runtime actions with adaptive learning to reduce noise
- Context‑aware risk judgment that prioritizes critical threats such as prompt injection, tool misuse, privilege escalation, and data exfiltration
- Automated remediation agent that creates pull requests, patches configurations, and validates fixes via integration with Claude Code, Lovable MCP, or CI/CD systems
- Security posture evaluation agent that benchmarks against standards, visualizes risk trends, and predicts emerging vulnerabilities
- Unified dashboard presenting a prioritized remediation plan (12 actionable controls) rather than raw alerts, with progress tracking and re‑assessment on each commit
- Seamless integration hooks for GitHub repositories, AI‑native IDEs, and agent platforms, enabling “security by design” from the first commit