Summary

The guide catalogues reusable architectures for agentic systems and gives a three-step way to choose one: define requirements, review the patterns, then match the pattern to the workload. It covers a single-agent baseline, eight multi-agent patterns, ReAct, human-in-the-loop and custom logic. Its central advice is to start with one agent and add structure only when a requirement demands it. Implementation pointers are Google-specific (ADK, Cloud Run, Agent Gateway/Identity/Registry), but the patterns themselves are general.

Key claims

  • Choose a pattern by four requirements: task characteristics (fixed steps or open-ended), latency, cost budget, and the need for human judgment.
  • Tasks that are predictable or need a single model call (summarise, translate, classify) are often better served without agents.
  • Start with a single agent. Its performance degrades as tools and responsibilities grow, and that is the signal to consider ReAct or multi-agent.
  • Workflow patterns (sequential, parallel, loop) use predefined control flow with no model orchestration, which makes them cheaper and faster but rigid.
  • Model-orchestrated patterns (coordinator, hierarchical decomposition, swarm) adapt at runtime but cost more calls, tokens and latency. Swarm is the most complex and costly.
  • Loop and swarm patterns need explicit exit conditions, or they risk runaway cost and non-convergence.
  • Every agent needs well-managed context: isolate it per agent, persist it across steps, compress large data.
  • Human-in-the-loop improves safety at critical points but requires building an external review system.
  • No evaluation guidance is given.

Feeds

Agent design patterns · Agent loop · Subagents · Context engineering · Verification and evals · Agent guardrails