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11 templates
A single-agent AI architecture where one agent handles everything: parsing requests, reasoning, calling tools via MCP, and generating responses. This is the default architecture for prototypes and simple automations—easy to debug but hits context-window limits quickly and is hard to parallelize. Ideal for MVPs and solo builders shipping fast.
The simplest AI-native architecture — a single agent that receives user input, reasons, plans, decides on tool calls, processes results, and generates responses. Direct MCP connection over stdio or HTTP. Best for MVPs and when low latency matters.
The simplest MCP pattern — direct connection between host application and MCP server over stdio or HTTP. No extra hops, lowest latency, easiest debugging. Perfect for MVPs, hackathons, and single-team setups where security governance is not yet a concern.
An API gateway pattern that sits between agents and MCP servers to handle authentication, rate limits, and auditing. The gateway enforces OAuth 2.0, SAML, SSO, tool-level rate limiting, and team-based quotas. Essential for multi-team or multi-tenant MCP deployments.
A routing pattern that puts a semantic router in front of MCP tools so the LLM only sees the subset it needs. Uses vector embeddings and cosine similarity to match user intent to tools dynamically. Achieves up to 96% reduction in input tokens when dealing with large tool catalogs.
A multi-agent mesh pattern where agents communicate through a shared context broker backed by MCP. Enables coordinated tool access and state synchronization across multiple specialized agents (planner, coder, reviewer, operator). Supports both orchestrated and choreographed interaction patterns.
A resilience pattern that wraps MCP calls with health-aware gates using three states: Closed (normal), Open (failures detected, fast-fail), and Half-Open (testing recovery). Prevents cascading failures when tools become unresponsive. Essential for production-grade reliability.
A caching and compression layer that sits between agents and MCP servers, intercepting redundant context requests before they hit the wire. Uses TTL-based cache invalidation, Brotli compression, and semantic caching. Can achieve up to 95%+ token reduction and significantly lower LLM bills.
Claw Code harness architecture diagram showing the user loop, Python orchestration layer, Rust runtime, and external systems such as the LLM provider, file system, and MCP server.
Zero-trust AI agent identity architecture with JWT validation, scoped OBO token exchange, SPIFFE/SVID workload identity, and MCP tool authorization.
MCP security gateway architecture that authenticates tool calls, routes execution into isolated sandboxes, sanitizes responses, and returns clean results to the AI agent.