Foundations of Scalable Multi-Agent Systems
A framework for reasoning across cooperating agents at scale.
We introduce a formal framework for designing, evaluating, and deploying multi-agent AI systems at production scale — covering communication, coordination, and emergent behavior. The framework is the conceptual basis for Zansoc’s Agent Platform and multi-agent technology layer.
Communication
Structured protocols with bounded token overhead.
Coordination
Planning across heterogeneous agents and tools.
Evaluation
Metrics for cooperation, robustness, and drift.
Deployment
Serving patterns for latency and cost.
Abstract
Multi-agent systems promise specialization and parallelism, but production deployments often collapse under unstructured chat, unbounded tool use, and unmeasured drift. This whitepaper proposes a layered architecture: typed messages, capability-scoped tools, hierarchical planners, and evaluation protocols that treat cooperation as a first-class property. We show how these abstractions map onto serving infrastructures that must meet latency and cost SLOs.
Communication under budget
We analyze token overhead as a function of team size and task horizon, then introduce schema-constrained channels that preserve expressivity for planning while preventing free-form chatter from dominating spend. Empirical sections compare blackboard, market, and hierarchical messaging on enterprise workflow benchmarks.
Coordination and authority
Not every agent should hold every tool. We formalize authority graphs and human-in-the-loop gates, then demonstrate how explicit escalation policies reduce unsafe actions without eliminating autonomy for routine subtasks.
Deployment notes
The final section translates the framework into runtime requirements — session fabrics, idempotent tool adapters, and monitoring for cooperative drift — now implemented in Agent Platform.
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