The Rise of Agent Swarms
Single-agent architectures are giving way to multi-agent systems — swarms of specialized agents that collaborate to accomplish complex tasks. A customer service swarm might include a triage agent, a technical support agent, a billing agent, and an escalation agent, all coordinating in real-time. A content moderation swarm might combine a text analysis agent, an image classifier, a policy evaluator, and a human-in-the-loop routing agent. These swarms are more capable than individual agents, but they introduce governance challenges that single-agent frameworks can't handle.
Governance Challenges in Multi-Agent Systems
Swarms create three governance problems that don't exist for individual agents. Emergent behavior: agents interacting with each other produce outcomes that weren't explicitly programmed into any single agent. Cascading violations: one agent's policy breach can propagate through the swarm before enforcement catches it. Accountability diffusion: when a swarm makes a bad decision, it's unclear which agent is responsible. Traditional agent governance treats each agent independently. Swarm governance must treat the swarm as a system — governing interactions, not just individual actions.
DRD's Swarm Governance Model
DRD introduces three primitives for swarm governance: Swarm Policies (governance rules that apply to the swarm as a whole, not just individual agents — e.g., 'no more than 3 agents may access the customer database simultaneously'), Interaction Contracts (pre-defined agreements between agents about what data they'll exchange and what actions they'll trigger — enforced at every handoff point), and Consensus Requirements (critical decisions require agreement from multiple agents before execution, preventing any single agent from taking high-impact actions unilaterally).
Consensus Voting Protocol
DRD's consensus voting protocol requires multiple agents to agree before high-stakes actions execute. The protocol works as follows: an agent proposes an action and submits it to the consensus pool. The pool routes the proposal to qualified voters — agents with relevant expertise and sufficient trust scores. Each voter independently evaluates the proposal against their local policy and returns an approve, reject, or abstain vote. The proposal executes only if it meets the quorum threshold (configurable per action type, default: 2/3 majority). All votes are recorded in DRD's tamper-evident audit log with timestamps and justifications.
Swarm Trust Scoring
Individual agent DRD Scores don't capture swarm-level trust. A swarm of five agents, each with a score of 85, might produce outcomes worse than a single agent scoring 90 — because the interaction effects aren't captured in individual scores. DRD calculates a Swarm Trust Score that accounts for individual agent scores (weighted by each agent's role in the swarm), interaction quality (how well agents communicate and coordinate), consensus track record (how often the swarm reaches agreement vs. deadlocking), and emergent behavior history (whether the swarm's collective outputs have stayed within policy). The Swarm Trust Score enables platforms to make access decisions about the swarm as a unit, not just its constituent agents.
Implementation: Swarm Registry
DRD's Swarm Registry is the entry point for swarm governance. Registering a swarm involves defining member agents and their roles within the swarm, specifying interaction contracts between agent pairs, setting swarm-level policies and consensus requirements, and establishing escalation paths for deadlocks and disputes. Once registered, the swarm operates under unified governance — DRD monitors individual agent actions, inter-agent communications, and swarm-level outcomes simultaneously. The swarm's trust badge reflects its collective governance posture, giving platforms a single trust signal for the entire multi-agent system.
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