AI Agent Design Template for AI Products

A single-agent system uses one LLM instance that can call multiple tools to complete tasks. A multi-agent system uses multiple specialized LLM instances that communicate with each other, where each agent handles a specific domain (e.g., one agent for search, another for code generation). Single-agent designs are simpler to build and debug. Multi-agent designs offer better specialization but introduce coordination complexity.

Start with the minimum set of tools needed to complete the top 3 user tasks. Each tool should have a clear, single responsibility. Avoid giving the agent tools it rarely needs, since more tools increase the chance of incorrect tool selection. Add tools incrementally based on observed user needs.

Layer multiple defenses. Use system prompts that instruct the agent to ignore instructions embedded in user content. Validate tool call parameters against expected schemas before execution. Apply output filtering to catch responses that reference system prompts or internal tool details. Red team your agent regularly with adversarial inputs.

Default to requiring approval for any action with side effects (creating, updating, or deleting data). As you build confidence through evaluation data, you can selectively make low-risk actions autonomous. The [AI governance glossary entry](/glossary/responsible-ai) covers frameworks for making these decisions systematically.

Build an end-to-end test suite with representative user tasks. For each task, define the expected sequence of tool calls and the expected output. Measure task success rate, average steps to completion, tool selection accuracy, and guardrail violation rate. Run adversarial tests separately to verify safety boundaries. The [AI Eval Scorecard](/tools/ai-eval-scorecard) provides a structured framework for agent evaluation.