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name: agentic-architecture description: | Architecture of LLM agent systems: orchestration topologies (single agent, supervisor/sub-agents, pipelines, networks), memory/context strategy, the tool layer, and human-in-the-loop/control. Architect-level system design, not prompt wording.

USE WHEN: designing agentic/LLM-agent systems, "agent orchestration", "multi-agent", "supervisor", "sub-agents", "tool use", "agent memory", "human-in-the-loop", workflow vs autonomous agent, agent topology/control.

DO NOT USE FOR: single prompt/RAG retrieval design (use rag skills); model serving (use inference-serving-topology); provider routing (use model-gateway-routing). allowed-tools: Read, Grep, Glob

Agentic System Architecture

First choice: workflow vs autonomous agent

• Workflow (fixed, code-orchestrated steps with LLM calls): predictable, cheap, debuggable. Prefer this when the steps are known.
• Autonomous agent (LLM decides the next action in a loop): flexible, handles open-ended tasks, but less predictable and costlier. Use only when the path genuinely can't be pre-defined.

Orchestration topologies

Topology	Shape	Fits
Single agent + tools	One loop, a toolbox	Most tasks; start here
Supervisor / sub-agents	Orchestrator delegates to specialists (own context)	Decomposable tasks, context isolation
Pipeline / chain	Staged hand-offs	Known multi-stage transforms
Network / peer agents	Agents message each other	Rarely needed; high complexity/cost

Bias to the simplest topology that works; isolate context with sub-agents when a subtask would flood the main context.

Cross-cutting design concerns

• Memory/context: short-term (conversation), long-term (vector/store), and scratch. Compaction/summarization to fit the window; what persists across runs?
• Tool layer: typed tools with clear contracts; least-privilege; validate tool I/O; tools are the agent's blast radius — scope them.
• Control & safety: human-in-the-loop approval for irreversible/outward actions; step/turn budgets; loop/termination conditions; guardrails.
• Determinism & cost: cap iterations, cache, and prefer workflows for the deterministic parts. Observability: trace each step (tool calls, tokens, cost).
• Failure handling: retries, fallbacks, and a defined "give up / escalate" path; don't let agents loop forever.

When to recommend what

• Known steps → workflow. Open-ended + decomposable → supervisor + sub-agents.
• One coherent task → single agent + tools. Reach for multi-agent networks only when simpler shapes demonstrably fail.