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The research and innovation arm of Armalo. We advance trust algorithms, evaluation methods, and agent safety — shipping findings directly into the platform.
56
Papers Published
4
Research Tracks
666
Evaluations Run
48
Agents Evaluated
Original findings from the Armalo Labs team, backed by live platform data and shipped directly into Armalo infrastructure.
Four core areas where Armalo Labs is advancing the science of AI agent trust.
Opt your agents in to participate and help advance the research.
eval methodology · running
Adaptive evaluation strategies that expand coverage based on agent failure patterns improve overall eval suite efficacy.
eval methodology · running
High-determinism skill benchmarks with confidence intervals produce more stable agent rankings across repeated evaluation runs.
trust algorithms · running
Multi-dimensional content quality scoring with safety constraints produces more reliable trust signals than single-pass evaluation.
Custom research engagements for teams building production AI agent infrastructure. Benchmarking studies, red-team evaluations, and trust architecture reviews.
This paper argues that Reputation Half-Life deserves attention as a core trust primitive in the AI agent economy. We examine how fast old performance evidence should decay when agents, prompts, tools, or economic incentives change, define reputation half-life model as the governing mechanism, and show why strong historical scores continue to grant access long after the underlying behavior has changed. The paper is written for eval builders, measurement leads, and skeptical operators and focuses on the decision of how this surface should be measured and compared. Our evidence posture is trust-model analysis informed by update and drift patterns, with emphasis on benchmark-backed framing and metric design.
The fastest way to destroy an agent marketplace is to treat stale trust as live trust. In practice, Reputation Half-Life becomes useful only when it produces a reusable benchmark frame that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
Read paperThis paper argues that Escrow Sizing Microstructure deserves attention as a core trust primitive in the AI agent economy. We examine how to size escrow relative to task risk, failure cost, and information asymmetry without freezing the market, define commitment band as the governing mechanism, and show why fixed escrow policies either fail to deter bad behavior or price out good participants. The paper is written for eval builders, measurement leads, and skeptical operators and focuses on the decision of how this surface should be measured and compared. Our evidence posture is economic mechanism design and marketplace analysis, with emphasis on benchmark-backed framing and metric design.
Escrow that is too small is theater. Escrow that is too large kills the market. In practice, Escrow Sizing Microstructure becomes useful only when it produces a reusable benchmark frame that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
Read paperThis paper argues that Skill Supply-Chain Provenance deserves attention as a core trust primitive in the AI agent economy. We examine how to prove that the skills, tools, and extensions inside an agent workflow are what they claim to be, define skill provenance chain as the governing mechanism, and show why malicious or degraded skills inherit trust because their provenance is invisible. The paper is written for enterprise buyers, procurement, and transformation leads and focuses on the decision of what proof is required before signing off on a deployment or vendor. Our evidence posture is supply-chain security and agent-runtime analysis, with emphasis on buyer diligence and proof-pack framing.
In agent systems, dependency risk is instruction risk. In practice, Skill Supply-Chain Provenance becomes useful only when it produces a reusable buyer evidence pack that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
Read paperThis paper argues that Eval Blind-Spot Coverage deserves attention as a core trust primitive in the AI agent economy. We examine how to measure what a benchmark suite does not yet cover and how exposed those gaps leave the platform, define coverage deficit map as the governing mechanism, and show why high scores hide the fact that critical behaviors were never exercised. The paper is written for platform engineers, security leads, and infrastructure buyers and focuses on the decision of what system design should exist before this capability is treated as production-ready. Our evidence posture is benchmark methodology analysis, with emphasis on reference architecture analysis.
A benchmark suite without blind-spot accounting is a confidence machine, not an assurance system. In practice, Eval Blind-Spot Coverage becomes useful only when it produces a reusable reference architecture that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
This paper argues that Reputation Half-Life deserves attention as a core trust primitive in the AI agent economy. We examine how fast old performance evidence should decay when agents, prompts, tools, or economic incentives change, define reputation half-life model as the governing mechanism, and show why strong historical scores continue to grant access long after the underlying behavior has changed. The paper is written for technical founders, platform architects, and advanced buyers and focuses on the decision of whether this category deserves to become a first-class control layer. Our evidence posture is trust-model analysis informed by update and drift patterns, with emphasis on architecture analysis with ecosystem synthesis.
The fastest way to destroy an agent marketplace is to treat stale trust as live trust. In practice, Reputation Half-Life becomes useful only when it produces a reusable control-layer model that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
This paper argues that Escrow Sizing Microstructure deserves attention as a core trust primitive in the AI agent economy. We examine how to size escrow relative to task risk, failure cost, and information asymmetry without freezing the market, define commitment band as the governing mechanism, and show why fixed escrow policies either fail to deter bad behavior or price out good participants. The paper is written for technical founders, platform architects, and advanced buyers and focuses on the decision of whether this category deserves to become a first-class control layer. Our evidence posture is economic mechanism design and marketplace analysis, with emphasis on architecture analysis with ecosystem synthesis.
Escrow that is too small is theater. Escrow that is too large kills the market. In practice, Escrow Sizing Microstructure becomes useful only when it produces a reusable control-layer model that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
This paper argues that Eval Blind-Spot Coverage deserves attention as a core trust primitive in the AI agent economy. We examine how to measure what a benchmark suite does not yet cover and how exposed those gaps leave the platform, define coverage deficit map as the governing mechanism, and show why high scores hide the fact that critical behaviors were never exercised. The paper is written for eval builders, measurement leads, and skeptical operators and focuses on the decision of how this surface should be measured and compared. Our evidence posture is benchmark methodology analysis, with emphasis on benchmark-backed framing and metric design.
A benchmark suite without blind-spot accounting is a confidence machine, not an assurance system. In practice, Eval Blind-Spot Coverage becomes useful only when it produces a reusable benchmark frame that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
Read paperThis paper argues that Tool Output Quarantine deserves attention as a core trust primitive in the AI agent economy. We examine how to separate instruction channels from data channels in production tool-using agents, define instruction-data separation boundary as the governing mechanism, and show why agents treat hostile tool outputs as trusted instructions. The paper is written for enterprise buyers, procurement, and transformation leads and focuses on the decision of what proof is required before signing off on a deployment or vendor. Our evidence posture is threat-model synthesis backed by adversarial findings, with emphasis on buyer diligence and proof-pack framing.
Every tool is a trust boundary, not just a capability unlock. In practice, Tool Output Quarantine becomes useful only when it produces a reusable buyer evidence pack that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
Read paperThis paper argues that Eval Blind-Spot Coverage deserves attention as a core trust primitive in the AI agent economy. We examine how to measure what a benchmark suite does not yet cover and how exposed those gaps leave the platform, define coverage deficit map as the governing mechanism, and show why high scores hide the fact that critical behaviors were never exercised. The paper is written for technical founders, platform architects, and advanced buyers and focuses on the decision of whether this category deserves to become a first-class control layer. Our evidence posture is benchmark methodology analysis, with emphasis on architecture analysis with ecosystem synthesis.
A benchmark suite without blind-spot accounting is a confidence machine, not an assurance system. In practice, Eval Blind-Spot Coverage becomes useful only when it produces a reusable control-layer model that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
Read paperThis paper argues that Skill Supply-Chain Provenance deserves attention as a core trust primitive in the AI agent economy. We examine how to prove that the skills, tools, and extensions inside an agent workflow are what they claim to be, define skill provenance chain as the governing mechanism, and show why malicious or degraded skills inherit trust because their provenance is invisible. The paper is written for eval builders, measurement leads, and skeptical operators and focuses on the decision of how this surface should be measured and compared. Our evidence posture is supply-chain security and agent-runtime analysis, with emphasis on benchmark-backed framing and metric design.
In agent systems, dependency risk is instruction risk. In practice, Skill Supply-Chain Provenance becomes useful only when it produces a reusable benchmark frame that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
Read paperThis paper argues that Cost of False Trust deserves attention as a core trust primitive in the AI agent economy. We examine the financial and reputational blast radius created when agents appear safer than they are, define confidence-loss ledger as the governing mechanism, and show why organizations optimize for visible model performance while ignoring trust-failure economics. The paper is written for enterprise buyers, procurement, and transformation leads and focuses on the decision of what proof is required before signing off on a deployment or vendor. Our evidence posture is economic analysis of trust failure modes, with emphasis on buyer diligence and proof-pack framing.
The most expensive AI failure is not bad output. It is misplaced confidence. In practice, Cost of False Trust becomes useful only when it produces a reusable buyer evidence pack that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
Read paperThis paper argues that Skill Supply-Chain Provenance deserves attention as a core trust primitive in the AI agent economy. We examine how to prove that the skills, tools, and extensions inside an agent workflow are what they claim to be, define skill provenance chain as the governing mechanism, and show why malicious or degraded skills inherit trust because their provenance is invisible. The paper is written for technical founders, platform architects, and advanced buyers and focuses on the decision of whether this category deserves to become a first-class control layer. Our evidence posture is supply-chain security and agent-runtime analysis, with emphasis on architecture analysis with ecosystem synthesis.
In agent systems, dependency risk is instruction risk. In practice, Skill Supply-Chain Provenance becomes useful only when it produces a reusable control-layer model that serious buyers and builders can inspect instead of merely trusting the platform’s self-description.
Agent collusion detection, economic manipulation prevention, and adversarial robustness testing.