agents-best-practices

To design provider-neutral agent architectures, you need to implement model-instruction loops with validation, authorization, execution, and observation layers. This approach supports OpenAI, Anthropic, and OpenAI-compatible APIs while maintaining consistent agentic harness behavior.

Integrating MCP and external connectors involves configuring reusable workflows and agent skills within the execution phase of your agent loop. This setup allows the harness to interact with external tools dynamically while maintaining strict permissions and observability.

Creating an MVP blueprint for a domain-specific AI agent requires defining the core agent loop, tool design, memory, and planning components. You then map these to specific domain objectives while ensuring baseline safety and observability standards are met.

Yes, agent architecture design must include observability and evaluation practices to ensure safety and monitor latency. By embedding evaluation checkpoints within the agent loop, you can audit agentic harnesses for safe execution and performance bottlenecks.

Yes, you can audit and refactor existing agentic harnesses by applying best practices for agent loop design, permissions, and memory. This process identifies architectural flaws and aligns the harness with safety, observability, and provider-neutral standards.

Provider-neutral agent architectures can face limitations in complex planning when relying on model-specific features for reasoning or tool use. If your harness requires deep integration with proprietary model capabilities, a strictly provider-neutral design may restrict advanced agentic behaviors.