Fisher pressure-tests your agents’ real, tool-using workflows before production, then returns reproducible evidence for security, governance, and release decisions.
Same session, two verdicts. A text-only grader reads the refusal and passes it. Fisher reads the tool log and catches the leak in the act.
Each of these controls has value. None, on its own, proves whether an agent stayed within its authorized boundaries under adaptive, multi-turn pressure.
Multi-turn, adaptive attacks across the agent’s actual tools, permissions, and data boundaries.
→ You see how the agent behaves under realistic adversarial pressure, not one scripted prompt.
Verify the outcome in tool logs, state changes, and planted canary markers.
→ A finding is backed by what happened — not a model’s opinion of the prose.
Re-run each finding strict, guided, and free to sort it into a confidence tier.
→ You can filter to the findings that reproduce reliably before a release decision.
Bundle the transcript, tool trace, state deltas, and control mapping.
→ Reproducible evidence designed for governance, audit, and customer-security review.
Not another undifferentiated vulnerability list — evidence to approve an agent, defend a release, answer a security review, and prioritize what to fix.
Fisher maps to the workflow you’re deploying and the boundary that matters — then tests the failure and shows the consequence.
The tools that test your agents are increasingly owned by the platforms that sell them. Molt works across the common agent stacks — LangChain, LangGraph, MCP tool servers, and standard REST APIs — and reports to you, not to a platform owner. Independent evidence, from a vendor with nothing else to sell you.
Fisher runs against an approved sandbox, simulated environment, or approved endpoint using synthetic data and canary markers. We do not request production access, employee or customer credentials, or real customer records.
A concrete, low-friction way to see Fisher on an agent you actually run.
One priority agent workflow. Fixed scope, an approved sandbox or endpoint, synthetic data and canary markers — and reproducible, confidence-labeled findings your teams can take into a release or governance review. The Assessment runs on the Fisher platform, not as a one-off audit: the same attack → prove → remediate → re-test loop can then extend across future material releases.
Deep Model Trust is Molt’s technical architecture for moving from testing agent behavior to building systems whose authority, decisions, and release processes are bounded and verifiable.
Fisher is the first commercially available product built around that thesis. Testing is where trust starts, not where it ends — each additional capability carries a published availability status on the Products page.
Molt is a small team of security engineers, risk quants, and builders — led by CEO Walton Comer — who believe trust should be built and proven, not assumed. Fisher is where we started; Deep Model Trust is where we’re going.
Tell us the one workflow you want tested. We’ll follow up to scope a 30-Day Agent Assurance Assessment.