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Multi-Agent Systems: Orchestration & Coordination
How multi-agent architectures work in practice — orchestration patterns, coordination strategies, and real-world cost tradeoffs.
Multi-agent systems use two or more AI agents working together to accomplish tasks that exceed the capability of any single agent. Agents can be orchestrated hierarchically, operate as peers, or run as specialists within a pipeline — each pattern with distinct tradeoffs in latency, cost, and reliability.
The posts below examine when multi-agent architectures actually make sense, the coordination patterns that work beyond demos, how to manage state and failure across agent boundaries, and the cost realities that catch teams off guard. We focus on production patterns, not theoretical frameworks.
Key topics include supervisor-worker topologies, parallel versus sequential orchestration, inter-agent communication protocols, shared memory strategies, and the observability requirements unique to distributed agent systems. If you’re considering moving from a single-agent architecture to multi-agent, these posts will help you evaluate whether the added complexity is worth it for your specific use case.
MCP gives your agent hands. A2A gives your agents colleagues. That distinction is now load-bearing: on April 23 2026 the Linux Foundation cut Agent2Agent Protocol v1.0 under the newly-formed Agentic AI Foundation (AAIF), the same governance body that took over MCP earlier this spring. The AAIF launch, announced jointly by OpenAI, Anthropic, Google, Microsoft, AWS, and Block with roughly 150 member orgs, settles the political question that has haunted multi-agent infrastructure since 2025: there is now one protocol stack, with one steward, that everyone has staked their roadmap on.
In our previous posts, we broke down the individual components of production AI agents: how the tool-calling loop works, how system prompts govern behavior, how MCP connects agents to business systems, and how to configure extension points in practice. Each of those posts examined a single agent doing a single job.
This post is about what happens when one agent isn’t enough.
2025 was the year of single AI agents. 2026 is the year they start working together. The AI agent market is growing at 46% year over year, and Gartner projects that 40% of enterprise applications will feature task-specific AI agents by the end of this year — up from less than 5% in 2025. But Gartner also predicts that over 40% of agentic AI projects will be canceled by 2027, and the primary killers are cost overruns, coordination complexity, and inadequate governance.