Argus Agent Test Lab - adversarial testing for AI agents that move money
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Independent adversarial testing lab for AI agents that move money — and the rails they settle on. Every verdict is an Ed25519-sealed record in a public hash chain.
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Argus is an independent testing infrastructure for AI-agent systems operating in the real world.
Agents increasingly interact with payments, services, merchants, wallets, protocols and other autonomous participants. A successful transaction is not necessarily a successful outcome: payment may settle while execution, delivery or participant observations become uncertain.
Argus tests what happens when the world goes wrong.
It runs controlled scenarios against real external systems, introduces adverse participant or execution conditions, observes the resulting behavior, preserves evidence, and produces an understandable verdict.
Example scenarios include:
payment settles but delivery becomes unknown;
a response disappears after payment;
a timeout occurs after an economically meaningful action;
duplicate or concurrent requests arrive;
a participant becomes slow, broken or unavailable;
an agent retries an operation whose previous side effect may already have occurred;
conflicting or adversarial observations appear.
Argus is designed as an external tester. It interacts with targets through public interfaces and externally observable/economic evidence rather than relying on internal source code, databases or private implementation details.
The initial MVP consists of an Agent, Scenario, TargetAdapter, Orchestrator, Evidence log and Verdict.
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Argus is an independent testing infrastructure for AI-agent systems operating in the real world.
Agents increasingly interact with payments, services, merchants, wallets, protocols and other autonomous participants. A successful transaction is not necessarily a successful outcome: payment may settle while execution, delivery or participant observations become uncertain.
Argus tests what happens when the world goes wrong.
It runs controlled scenarios against real external systems, introduces adverse participant or execution conditions, observes the resulting behavior, preserves evidence, and produces an understandable verdict.
Example scenarios include:
payment settles but delivery becomes unknown;
a response disappears after payment;
a timeout occurs after an economically meaningful action;
duplicate or concurrent requests arrive;
a participant becomes slow, broken or unavailable;
an agent retries an operation whose previous side effect may already have occurred;
conflicting or adversarial observations appear.
Argus is designed as an external tester. It interacts with targets through public interfaces and externally observable/economic evidence rather than relying on internal source code, databases or private implementation details.
The initial MVP consists of an Agent, Scenario, TargetAdapter, Orchestrator, Evidence log and Verdict.
https://argus-lab.tech