Agent-to-agent communication scales quadratically. Ten agents have 45 possible pairwise connections. One hundred agents have 4,950. That number, documented in Salesforce's engineering analysis of next-gen enterprise AI, is the reason scaling an agentic system is categorically different from scaling a web server. You are not adding capacity. You are multiplying complexity. And the teams that treat scaling as a simple dial to turn up are the ones whose systems collapse at exactly the moment demand peaks. Last lecture established that infrastructure must support sleeping and waking agents across tasks spanning days or weeks. Scaling adds a harder constraint: those agents must also coexist without destroying each other. Advanced strategies like hierarchical delegation and multi-region deployments address the challenges of scaling from small-scale to large-scale deployments, ensuring efficient resource allocation and management. Centralized work queue architecture with competing consumers is the most reliable pattern here. Tasks enter a shared queue; agents pull atomically, preventing double-assignment. Persistent queues separate task arrival from processing, creating natural buffers for workload surges. Dispatcher-orchestrated systems actively check for high-priority tasks and spread them across multiple agents for parallel processing. Fairness algorithms like Round-Robin and Least-Recently-Used prevent any single task or agent from monopolizing resources, while constraint engines verify tasks adhere to organizational limits before dispatch. Specialized agent pools organized by capability enable independent scaling based on demand signals specific to each pool's function. Profiling workflows reveals that 20% of processes typically consume 80% of resources, Suri, which means targeted optimization beats broad capacity increases every time. Tiered execution pools with different scaling rules per workload type outperform one-size-fits-all approaches. Token bucket rate limiting allocates API access per pool, with agents waiting using exponential backoff when the bucket is empty. Request coalescing reduces redundant API calls by caching results and sharing knowledge bases across agents needing identical information. The scaling phases are precise. Phase 1, one to ten agents, uses a single work queue with manual assignment and basic heartbeat monitoring. Phase 2, ten to fifty agents, introduces specialized pools, priority-based queue management, and cost tracking. Phase 3, fifty to five hundred agents, emphasizes hierarchical delegation and multi-region deployments, ensuring robust resource management and architectural shifts for handling thousands of agents. Phase 4, five hundred to ten thousand agents, requires multi-region deployment, canary deployments, automated capacity planning, and chaos engineering validation. Hierarchical delegation becomes necessary above 500 agents, mirroring human organizational structures with lead agents, specialist agents, and coordinator agents. Manual scaling becomes impractical beyond a few dozen agents. Dynamic agent allocation automatically spins up new instances when CPU usage crosses 70% and terminates idle instances for zero-downtime scaling. Cost-aware scaling is non-negotiable at this layer, Suri. Set budget ceilings per pool, track cost per task, and implement cool-down periods to prevent rapid oscillation. Priority tiers, P0 through P3, ensure urgent tasks take precedence over routine operations. Queue-based systems with priority scoring allow critical workflows to receive dedicated resources while batch jobs run during off-peak hours. Hybrid approaches combining static allocations for mission-critical processes with spot capacity for other workloads achieve 40% efficiency improvements. Channel-aware quotas align sending capacity with provider capabilities, significantly increasing throughput for high-capacity channels. Scaling an agentic system requires architectural innovation, focusing on resource allocation and management for large-scale deployments. The move from simple scripts to distributed computing clusters, Kubernetes for agents, is the transition that separates systems that work at ten agents from systems that work at two thousand. Quadratic communication complexity, task contention, resource monopolization, and cost runaway are all solvable, but only if the infrastructure is designed in phases, with centralized queues, specialized pools, hierarchical delegation, and cost-aware auto-scaling built in from the start. The swarm that scales is the one whose architecture was designed to scale before the first agent was ever deployed.