This article outlines the MIGHT Stack (Model, Inference, Gateways, Harness, Tools), a modular architecture that allows developers to leverage open source models for agentic software without needing deep machine learning expertise or dedicated hardware.
- The Model layer acts as the intelligence component, with large models like Kimi K3 suited for complex reasoning and small models like GLM 5.3 Flash offering speed and cost efficiency for narrow tasks.
- Inference providers host these models via API, allowing developers to pay per token and experiment with different providers without managing infrastructure.
- Gateways and routers aggregate multiple inference providers behind a single API, enabling seamless switching between models and backends based on cost or performance.
- Harnesses manage the interaction between the user, the codebase, and the model, executing tools and commands decided by the model to perform actual work.
This separation of concerns enables developers to experiment with new models rapidly and customize each layer of their development workflow independently.