Your AI doesn’t need to crash to cause damage.

No error logs. No stack traces. Just wrong outputs that look right: bad guidance to customers, brittle agent behavior, incorrect decisions, silent failures, or routing and memory issues that only appear once the system is used for real work.

Hermes Labs helps teams find and harden those failure points before users, customers, or auditors do.

Hermes Labs builds and applies AI reliability infrastructure for the layer between demo and deployment: evaluations, routing, memory, agent behavior, runtime controls, and evidence-backed system improvement.

What we do:

AI Reliability Reviews
We probe LLM systems, agent workflows, and AI-enabled products for structural failure modes standard evals miss. You get ranked findings, reproducible test cases, and concrete remediation guidance.

Stack Diagnostics & Remediation
We inspect and improve prompts, agent scaffolds, tool definitions, routing logic, retrieval and memory systems, SDK integrations, and runtime controls.

Evidence & Assurance Infrastructure
We help teams produce the technical artifacts needed to explain what their AI system did, why it failed, what changed, and what evidence supports the fix.

Why us:

• 2,000+ controlled adversarial evaluations across current-generation LLMs
• 5 US patent applications
• Published research on null-result asymmetry in LLMs and epistemic failure modes
• Major upstream contributions to LangChain and Microsoft Semantic Kernel, including Anthropic integration fixes and chat-history reducer fixes that preserve system/developer instructions
• Open-source infrastructure across memory, routing, prompt auditing, runtime safety, evaluation, and CI quality gates

Have questions about your AI system, agent workflow, or model stack? Message us with what you’re building and your pain point.

Hermes Labs

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Hermes Labs

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