Agent architecture determines what an AI coding agent can actually do. Two agents using the same LLM can produce dramatically different results because their underlying architectures differ. Here are the 7 key dimensions we evaluate.
Can the agent decompose complex tasks and coordinate sub-agents? Claude Code's Coordinator mode spawns parallel sub-agents with shared caching. Simpler agents process tasks sequentially. This is the single biggest differentiator in architecture quality.
Does the agent remember what it did across sessions? The best agents use Multi-layer memory with consolidation — like Claude Code's auto-dream system that merges and prunes memories in the background. Basic agents start fresh every session.
What can the agent do? File operations, terminal execution, web search, code editing, MCP integration. The breadth and depth of the tool ecosystem directly impacts how much the agent can accomplish autonomously.
These dimensions cover cost optimization, security guardrails, error recovery, and ecosystem health. Together they form the complete architecture picture that our rubric measures.