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Curated Collection
Posts that connect directly to Armalo Labs research and benchmarks.
Topics: research-backed · agent-evaluation · provenance
AI agents fail their commitments in production at rates enterprises aren't measuring. Behavioral drift, hallucination under pressure, scope creep, capability misrepresentation — and zero accountability infrastructure to catch any of it. Here's the evidence, and here's the fix.
The AI safety conversation is dominated by alignment research. But deployed agent reliability — the problem most organizations face today — is an incentive design problem that can be solved now with existing tools.
After helping dozens of enterprises deploy AI agents in production, we've seen the same failure patterns repeat. This is what actually goes wrong — and the infrastructure decisions that prevent it.
An agent that has handled real value under real consequence carries a different kind of evidence than one with only abstract evaluations. Markets should reflect that.
The strongest agents in a demo are not always the safest agents in production. Trust grows from operational evidence, not polished peak performance.
Cross-platform trust is appealing, but a signed credential is not enough. Receiving systems need freshness, provenance, and a clear revocation path.
A calm-environment evaluation can make an agent look excellent. The first real trust test arrives when demand spikes, latency stretches, and the system has to degrade gracefully.
Sybil resistance, cross-platform score portability, adversarial trust gaming, privacy-preserving verification. The hardest unsolved problems in agent trust.
How to evaluate AI agents under adversarial load, ambiguous inputs, and realistic production pressure rather than only under clean benchmark conditions.
The AI systems that matter long-term are not the ones with the best demos — they are the ones that improve themselves while you sleep. Armalo applies Karpathy's autoresearch philosophy to build a trust evaluation infrastructure that gets measurably better every night, creating a compounding data moat that no competitor can close by throwing more engineers at the problem.
What gets harder next for cross-agent memory handoff as agent systems become more networked, autonomous, and economically consequential.
What gets harder next for AI agent supply chain trust as agent systems become more networked, autonomous, and economically consequential.
A realistic deployment story showing what changes operationally and commercially once cross-agent memory handoff is implemented well.
A realistic deployment story showing what changes operationally and commercially once AI agent supply chain trust is implemented well.
Karpathy Autoresearch Recursive Self Improvement Superintelligent AI Agents matters because serious agent systems need system design across trust, memory, and orchestration, not just better demos. This piece tackles contrarian thought leadership for readers deciding which unresolved questions deserve investigation before full commitment, especially when many agent stacks can coordinate tasks or host runtimes, but far fewer can preserve trust, evidence, and compounding behavior across long-hor...
The governance and policy model behind cross-agent memory handoff, including grant, review, override, revocation, and audit controls.
The governance and policy model behind AI agent supply chain trust, including grant, review, override, revocation, and audit controls.
A strategic map of hermes agent benchmark across tooling, control layers, buyer demand, and what the category is likely to need next.
A strategic map of ai agent benchmark leaderboards across tooling, control layers, buyer demand, and what the category is likely to need next.
Karpathy Autoresearch Recursive Self Improvement Superintelligent AI Agents matters because serious agent systems need system design across trust, memory, and orchestration, not just better demos. This piece tackles category shaping for readers deciding where the category is headed and which surfaces are still open to own, especially when many agent stacks can coordinate tasks or host runtimes, but far fewer can preserve trust, evidence, and compounding behavior across long-horizon workflows.
How cross-agent memory handoff changes incentives, payment risk, recourse, and commercial behavior once trust becomes economically real.
How AI agent supply chain trust changes incentives, payment risk, recourse, and commercial behavior once trust becomes economically real.
A leadership lens on hermes agent benchmark, focused on operating leverage, downside containment, evidence quality, and why executive teams should care before an incident forces the conversation.
A leadership lens on ai agent benchmark leaderboards, focused on operating leverage, downside containment, evidence quality, and why executive teams should care before an incident forces the conversation.