Molt AI provides enterprise AI agent assurance through Fisher, an adversarial red-teaming platform that pressure-tests tool-using agents with adaptive multi-turn attacks. Fisher verifies outcomes by examining actual tool calls and state changes — not just conversational responses — then replays confirmed findings to produce reproducible, confidence-labeled evidence for security and governance teams.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying AI agents with access to files, databases, mail, and APIs face significant security exposure, yet most red-team testing evaluates only the agent's final text response. A fluent refusal can conceal tool calls that already executed a forbidden task, leaving dangerous data access or state changes undetected. Traditional evaluation methods fail to catch multi-turn manipulation, instruction-hierarchy confusion, and boundary crossings that can lead to data leakage or unauthorized actions.
Solution
Molt AI offers Fisher, an adversarial agent red-teaming platform that safely attacks real, tool-using AI workflows to identify exploitable weaknesses before attackers do. Fisher runs adaptive, multi-turn attack campaigns against an explicit target contract, probing permissions, data boundaries, and authorization gaps across a process that includes probe, prove, replay, remediate, re-attack, and learn phases. The platform evaluates tool calls, retrievals, writes, and boundary crossings, then replays confirmed findings to verify they reliably reproduce, delivering proof — not opinion — of agent behavior. Each finding is documented with the conversation, tool calls, state changes, severity, and confidence levels needed for security, governance, and release review.
Target Audience
Primary customers are security and AI-governance teams at enterprises deploying tool-using AI agents in high-consequence workflows, including fintech, healthcare, and enterprise SaaS companies.
Features
- Adaptive multi-turn attack engine that evolves strategies across 50,000+ episodes and 500,000+ conversation turns
- Action-level verification that reads tool logs and state changes rather than transcripts alone, catching failures text-only graders miss
- Confirmed finding replay with exact, guided, and free provenance to test reproducibility and reliability
- Remediation verdict tracking marked as fixed, partial, bypassed, or unknown, with re-attack capability
- Ready-made scenario library covering customer support, coding assistants, RAG agents, database/SQL agents, financial workflows, PII/PHI protection, telecom, RBAC, and multi-agent systems
- Framework mapping to standards like OWASP Agentic for governance context
- Tests runs against approved sandboxes or endpoints with synthetic data and canary values, never production access
- 30-Day Agent Assurance Assessment delivers reproducible, framework-mapped evidence for release decisions