The Topology of an LLM Agent Loop With Tool Use

AI agents can be understood through their topology: the location and arrangement of the model, agentic loop, and tools, with the latter two forming the harness. Different products distribute these components across local devices, hosted servers, virtual machines, browsers, and private cloud infrastructure, creating tradeoffs in capability, user experience, cost, privacy, and security. Hosted models offer convenience and performance but require trust in vendors and raise concerns about data use, model tampering, and unpredictable token costs, while local models can better protect sensitive data and support bring-your-own-model pricing. The agentic loop determines how conversations, histories, interfaces, attachments, feedback, and integrations are managed, making specialized loops often more useful than general-purpose chatbot interfaces for tasks such as coding or operations. Tools give agents their practical power, but code execution and external connections introduce security risks that can be mitigated through virtual machines, browser or WASM sandboxes, restricted database access, and careful management of credentials and OAuth tokens. As more products embed agents and expose capabilities through standards such as MCP, understanding where each component runs helps users and builders assess an agent’s limits, risks, and potential.

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