Founder of Hermes Labs, an AI reliability engineering lab building tools, research, and autonomous infrastructure for agents and LLM systems.
I work on the operational language layer of AI systems: instructions, tool interfaces, retrieval, memory, prompt injection, runtime policy, evaluation, and the evidence needed to reconstruct what an agent actually did.
Start here: Hermes Labs · GitHub org · LintLang · Research · Contribution ledger
- LintLang: static linter for agent configs, tool descriptions, and system prompts; no LLM call. lintlang.ai · playground
- Little Canary: prompt-injection detection through sacrificial canary-model probes. littlecanary.ai
- Fidelis Memory: agent memory that returns original passages verbatim instead of a paraphrase.
- Hermes Rubric: evidence-first LLM-as-judge scoring; every dimension ties to a quoted file:line, hedged on thin evidence.
- Hermes Blind: recovers the original goal of a long agent session from its first turn, for multi-turn drift recovery.
- zer0dex: local dual-layer memory pattern for agents, pairing a markdown index with local semantic retrieval.
Full catalog with evidence boundaries: hermes-labs.ai/open-source
71 Upstream Contributions · 6 Integrations & Adoption outcomes (as of 2026-10-20).
Engineering and technical documentation accepted by external projects, plus reports that led to implemented upstream fixes.
- microsoft/semantic-kernel #13610 — Preserve the first system/developer message during Python chat-history truncation and handle the target_count=1 boundary. Case study.
- langchain-ai/langchain #35544 — Drop forced tool_choice with a warning when Anthropic extended thinking is enabled; preserve auto and unaffected requests. Case study.
- microsoft/semantic-kernel #13635 — Use value equality to avoid duplicate null entries in strict JSON Schema type arrays, with three regression tests. Case study.
- stanfordnlp/dspy #9978 — Reject an empty Evaluate devset with a descriptive ValueError before metric-summary division, with a regression test. Case study.
Working integrations accepted upstream, and independent developers building on our tools and ideas.
- LintLang in other workflows: Character.AI Larch CI and independent Gentoo Haven packaging.
- Little Canary in other projects: Orbit's agent adapter and Preflight's independently adapted inspection techniques.
- zer0dex-inspired memory architecture: independent implementations in AllSource and Mnemosyne.
Full record and attribution · Structured ledger · Verified 2026-10-20. Listings and pending submissions are excluded from these totals.
Many contributions from this account are independently discovered and executed by Hermes Labs' autonomous engineering infrastructure, rather than beginning with me selecting or prompting the specific task. I set objectives, operating constraints, and authorization boundaries; steer or review where needed; and remain the responsible human for work published from this account. The machinery is part of the experiment: can an AI-native engineering institution notice useful work, investigate it, make bounded changes, preserve evidence, and stop appropriately without requiring a human to originate every individual action?
Hermes Labs publishes research and technical notes on AI reliability, epistemic failure, measurement validity, agent-tool semantics, prompt injection, and the limits of model self-report. Current work includes Tool Differentia, Behavioral Canarying for Prompt Injection, The Generative Horizon, Precise Records, Unstable Meanings, A Taxonomy of Epistemic Failure Modes in Large Language Models, and The Asymmetric Burden of Proof.
- Tool Differentia: Relational Static Analysis for AI Agent Tool Descriptions
- Behavioral Canarying for Prompt Injection
- The Generative Horizon
- Precise Records, Unstable Meanings
- A Taxonomy of Epistemic Failure Modes in Large Language Models
- The Asymmetric Burden of Proof
hermes-labs.ai/research · hermes-labs-ai/hermes-publications
Hermes Labs emerged from an exploration of philosophy of language, phenomenology, and hermeneutics applied to AI systems. The practical consequence became an engineering thesis: in agent systems, language is part of the runtime. System prompts, tool descriptions, retrieved passages, memory, summaries, policies, and evaluation criteria do not merely describe a system. They participate in what it notices, chooses, remembers, and does. Hermes Labs treats that layer as an engineering surface: something that can be inspected before deployment, tested under adversarial conditions, controlled at runtime, and verified afterward.