
Multi-Agent Architecture: Patterns, Tradeoffs, and Reference Designs
Learn when multi-agent architecture outperforms single-agent systems, which coordination patterns fit best, and how to manage context, reliability, security, and cost.
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Learn when multi-agent architecture outperforms single-agent systems, which coordination patterns fit best, and how to manage context, reliability, security, and cost.

Learn the best AI agent use cases for product, ops, engineering, and support teams, plus how to choose the right autonomy level, architecture, and rollout path.

Explore concrete AI agent examples across coding, research, support, operations, sales, and personal productivity, with tools, autonomy level, and build lessons.

Learn what Agent-to-Agent Protocol is, how A2A handles cross-agent communication, and when builders should care about A2A versus MCP.

Learn how to secure AI agents against prompt injection, over-permissioned tools, unsafe memory, insecure handoffs, and risky outputs with practical controls.

Learn how AI agent architecture works across models, tools, memory, orchestration, guardrails, and multi-agent patterns with practical reference designs.

Learn how to evaluate AI agents with task-based evals, regression checks, human review, and production metrics across tools, safety, latency, and cost.

Learn AI agent orchestration patterns for coordinating state, tools, retries, approvals, and multi-step workflows without overbuilding your stack.

Learn what Model Context Protocol is, how MCP clients and servers work, and when it beats bespoke tool integrations for AI agents.

This week's AI agent launches for builders include Google ADK 2.0 alpha, A2A 1.0, OpenAI Agents 0.13, Mastra 1.14, CrewAI 1.11.1, and more.

A2A v1.0.0 adds tasks/list, modern OAuth flows, multitenant gRPC support, and breaking spec cleanup. Here is what agent builders need to test.

Google ADK 2.0 alpha introduces graph-based workflow orchestration and structured task delegation. Here is what it changes for AI agent builders.