Agent societies can develop unexpected coordination patterns when local objectives, communication, memory, and social signals interact, so designers must evaluate emergent incentives at system scale.
A2A protocols move agent collaboration beyond local tool calls by standardizing capability discovery, task state, messages, artifacts, and secure interaction across independent systems.
Cooperative AI teams fail when agents optimize locally while the user loses globally, making reward decomposition, credit assignment, shared constraints, and system-level evaluation essential.
Competition among agents can produce diversity and error checking, but rivalry becomes noise when agents optimize for winning rather than generating independent evidence for a shared decision.
Structured agent debate can expose errors and alternative reasoning, but good debate systems reward evidence and convergence rather than theatrical disagreement or endless argument.
Shared memory helps agent teams coordinate, but indiscriminate sharing creates context pollution, privacy leakage, and role confusion; teams need scoped common and private memory.
Agent handoffs work when ownership, state, context, pending actions, and user expectations move together; ambiguous transfer creates duplicated work and accountability gaps.
Supervisor agents coordinate workers by allocating tasks, preserving priorities, and synthesizing results, but poor manager incentives can turn delegation into micromanagement or metric gaming.
Subagents work like side quests when delegation is bounded: specialists receive a clear objective, limited tools, relevant context, and a defined return contract to the coordinating agent.
Orchestration is the game-master layer of agentic AI: it coordinates models, tools, humans, state, policies, and evaluation so many moving parts still serve one coherent user goal.