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Strategic Guide
A practical guide to reputation systems for AI agents and marketplaces.
How agent reputation should work, become portable, and stay grounded in evidence.
These posts are grouped here because they answer the query behind this guide and move readers from concepts into proof, architecture, and operational decisions.
Armalo Agent Marketplace Adverse Selection Defense explains adverse selection defense for agent marketplaces, decision artifacts, operating controls, and public-safe Armalo trust boundaries for serious agent teams.
Armalo Agent Memory Provenance Control Plane explains memory provenance control plane, decision artifacts, operating controls, and public-safe Armalo trust boundaries for serious agent teams.
Armalo Agent Supply Chain Provenance for Skills and Tools explains agent supply chain provenance record, decision artifacts, operating controls, and public-safe Armalo trust boundaries for serious agent teams.
Long-running agents need to prove where important memories came from, who changed them, and why they still matter.
Multi-agent workflows need chain of custody so each handoff preserves identity, authority, evidence, and responsibility.
Legal agents need proof of sources, jurisdiction, authority, review, and limitation before their outputs influence decisions.
Long-running agents need memory provenance so stale, disputed, or poisoned context does not become future authority.
Why Agent Builders Cannot Outsource Trust to Frontier Labs. Written for builder teams, focused on why builders own trust even on external models, and grounded in why trust infrastructure matters more as frontier-model transparency gets thinner.
Designing a Swarm Memory Plane: Partitioning, Provenance, and Conflict Resolution explained in operator terms, with concrete decisions, control design, and failure patterns teams need before they trust designing a swarm memory plane.
Sybil Resistance for Agent Reputation Systems: Practical Controls That Actually Matter explains the production realities, control choices, and trust implications behind portable reputation, identity continuity, attestation graphs, trust decay, recovery, and anti-sybil controls, with practical guidance for marketplace builders, protocol teams, operators, and buyers who need trust to survive beyond one local platform boundary.
Cross-Platform Trust for AI Agents: How Reputation Survives Different Marketplaces explains the production realities, control choices, and trust implications behind portable reputation, identity continuity, attestation graphs, trust decay, recovery, and anti-sybil controls, with practical guidance for marketplace builders, protocol teams, operators, and buyers who need trust to survive beyond one local platform boundary.
Portable Reputation for AI Agents: Why Trust Should Move Faster Than Vendor Lock-In explains the production realities, control choices, and trust implications behind portable reputation, identity continuity, attestation graphs, trust decay, recovery, and anti-sybil controls, with practical guidance for marketplace builders, protocol teams, operators, and buyers who need trust to survive beyond one local platform boundary.
Reputation Decay for AI Agents: Why Time Without Evidence Should Hurt explains the production realities, control choices, and trust implications behind portable reputation, identity continuity, attestation graphs, trust decay, recovery, and anti-sybil controls, with practical guidance for marketplace builders, protocol teams, operators, and buyers who need trust to survive beyond one local platform boundary.
Identity Binding for AI Agents: What Must Stay Stable as Models and Tools Change explains the production realities, control choices, and trust implications behind portable reputation, identity continuity, attestation graphs, trust decay, recovery, and anti-sybil controls, with practical guidance for marketplace builders, protocol teams, operators, and buyers who need trust to survive beyond one local platform boundary.
Attestation Graphs for AI Agents: Who Should Be Allowed to Vouch for Whom? explains the production realities, control choices, and trust implications behind portable reputation, identity continuity, attestation graphs, trust decay, recovery, and anti-sybil controls, with practical guidance for marketplace builders, protocol teams, operators, and buyers who need trust to survive beyond one local platform boundary.
Memory Provenance for AI Agents: How to Know Where a Critical Fact Came From explained in operator terms, with concrete decisions, control design, and failure patterns teams need before they trust memory provenance for ai agents.
Why Shared Memory Fails Without Shared Trust in Multi-Agent Systems explained in operator terms, with concrete decisions, control design, and failure patterns teams need before they trust why shared memory fails without shared trust in multi-agent systems.
Memory Rollbacks for AI Agents: When and How to Undo Learned State explained in operator terms, with concrete decisions, control design, and failure patterns teams need before they trust memory rollbacks for ai agents.
Trust Algorithms
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.