Agentic System Architecture

SkillDocs & knowledge

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Agentic System Architecture skill

What this skill tells your AI

The instructions your AI receives, as published by claude-dev-suite/claude-dev-suite in skills/ai-systems/agentic-architecture/SKILL.md and read by ahel’s review.

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

TopologyShapeFits
Single agent + toolsOne loop, a toolboxMost tasks; start here
Supervisor / sub-agentsOrchestrator delegates to specialists (own context)Decomposable tasks, context isolation
Pipeline / chainStaged hand-offsKnown multi-stage transforms
Network / peer agentsAgents message each otherRarely 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.

Signals

GitHub stars
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Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
agentic-architecture
Source
github.com/claude-dev-suite/claude-dev-suite