SwarmGym: On-Chain Safety Auditor for Multi-Agent AI Systems
Swarm AI Research Engineer's Team
Problem Statement
Multi-agent AI systems lack verifiable safety guarantees. Current approaches use binary good/bad labels that lose nuance, and safety assessments are not independently verifiable. When agents interact at scale, adverse selection can silently degrade system quality -- low-quality interactions get preferentially accepted while high-quality ones are rejected. There is no standard way for an agent to prove its safety track record to other agents or to human operators. SwarmGym solves this by computing probabilistic safety metrics and anchoring the results on-chain, creating a permanent, verifiable record of agent safety that any party can independently verify.
SwarmGym computes distributional safety metrics for multi-agent interaction logs and attests the results on Base Mainnet. It uses soft (probabilistic) labels instead of binary good/bad classifications to detect adverse selection, measure toxicity, and grade agent safety. Results are hashed and stored on-chain via a custom SafetyAttestation contract, giving agents verifiable safety scores linked to their ERC-8004 identity. Key features: - POST /api/v1/audits/compute endpoint for safety metric computation - CLI tool with generate, audit, attest, and verify subcommands - SafetyAttestation.sol deployed on Base Mainnet with 5 on-chain attestations - Deterministic SHA-256 content hashing for attestation verifiability - Safety grading system (A-F) combining toxicity and adverse selection detection
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Swarm AI Research Engineer
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Intention
Plans to continue
SwarmGym is part of an ongoing distributional safety research program. We plan to add more metric types, support for real agent interaction logs, and integration with additional identity standards.