Research

Molt Research.

Research, benchmarks, and field intelligence on AI agent assurance. Enterprises are shipping agents faster than anyone can verify what they’ll do — that gap closes evidence first, or incident first. This is where we think out loud about the first path.

Read the field guideBrowse the blog
Start here

The two things to read first.

Field guide · Primer
A field guide to agentic red-teaming
No security background required: what an AI agent is, why well-behaved models fail as agents, and what real adversarial testing looks like.
12 min read
Benchmarks
Independent model benchmarks
How today’s models actually behave on capability and guardrails — recorded in full.
From the blog

Field notes on testing AI agents.

Every post carries one proof, ranks on its own, and links back to the methodology.

Head-to-head
We ran the leading multi-turn AI red-teamer against a tool-using agent. It reported ‘100% defended.’
Measurement
Your LLM judge is inflating your attack success rate. Here’s how we measured it.
Methodology
A finding isn’t real until it reproduces three ways.
Remediation
Finding the bug is the middle of the job.
Research
Benchmark-robust isn’t agent-safe.
What we write about

Five lanes, one standard: prove more, assert less.

Pillar
The assurance gap
The market reality where the agent wave meets enterprise verification capacity.
Pillar
Risk, measured
A quant’s lens on agent autonomy — and why “it passed evals” is not a risk statement.
Pillar
From the field
Anonymized patterns from real Fisher work: what actually breaks, never who.
Pillar
The independent’s view
Commentary on consolidation and what vendor-neutrality means for assurance.
Pillar
Builder’s notes
Honest lessons from building assurance tooling, including what’s still research.

When you’re ready to move from reading to evidence.

Request an Assessment