Summary
Six composable design patterns for building LLM-powered agentic systems, from simple augmentation to autonomous agents. Anthropic’s key insight: the most successful implementations use simple, composable patterns rather than complex frameworks.
Key Points
- Workflows vs Agents: Workflows use predefined code paths; agents let the LLM dynamically direct processes. Start with workflows, escalate to agents only when needed.
- Simplicity first: Agentic systems trade latency and cost for performance — don’t add complexity unless the task demands it
The Six Patterns (increasing complexity)
- Augmented LLM — LLM + retrieval + tools + memory. The building block for everything else.
- Prompt Chaining — Sequential steps with programmatic gates between them. Good for decomposable, fixed-structure tasks.
- Routing — Classify input, dispatch to specialized handler. One decision point, multiple paths.
- Parallelization — Run subtasks simultaneously. Two flavors: sectioning (split task) and voting (redundant runs for consensus).
- Orchestrator-Workers — Central LLM dynamically delegates to workers. Unlike chaining, subtasks are determined at runtime.
- Evaluator-Optimizer — Generator + evaluator in a loop. GAN-inspired: iterate until quality threshold met.
Three Principles for Agent Design
- Simplicity — resist adding layers; simple patterns compose well
- Transparency — expose planning steps to users
- Agent-Computer Interface (ACI) — tool documentation is as important as prompts; tool design deserves equal prompt engineering effort
Tool Design
- Tool definitions deserve as much engineering as system prompts
- Mirror natural language; eliminate unnecessary formatting
- Include examples and clear boundaries
- Anthropic spent more time on tool design than prompts for SWE-bench
Relationship to Harness Engineering
These patterns describe the internal architecture of agents, while harness engineering describes the external controls around agents. They’re complementary:
- Patterns = how the agent is structured internally
- Harness = how the environment constrains and validates the agent
The evaluator-optimizer pattern is directly related to Anthropic’s GAN-inspired multi-agent harness for long-running tasks.
Open Questions
- When to compose multiple patterns vs. keeping it simple?
- How do these patterns interact with Meta-Harness automated optimization?
- What metrics determine when to escalate from workflow to agent?
Evidence Timeline
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2026-04-10: “Claude Code from Source” book — Claude Code implements all 6 agentic patterns plus recursive sub-agent architecture, fork agents for cache sharing, and swarm teams with mailbox messaging.
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2026-04-07: Initial compilation from Anthropic’s “Building Effective Agents” (Schluntz & Zhang, 2024-12-19)
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2026-04-07: Added cross-ref to tool-use-as-meta-ability — tool use is the foundation of the Augmented LLM pattern