The Interpretable Context Methodology (ICM) replaces traditional AI orchestration frameworks like LangChain or CrewAI with a directory-based structure. This approach organizes tasks into sequential steps using markdown and JSON files, allowing agents to process information in a modular way similar to Unix pipelines.
The system uses a five-layer hierarchical context structure. Layer 0 handles global identity and routing, while subsequent layers manage stage-specific instructions, reference materials, and output artifacts. This design ensures that agents only load necessary context, keeping the load under approximately 5.6k tokens compared to 42k tokens in monolithic setups.
ICM emphasizes human-in-the-loop interaction, enabling users to inspect, edit, and verify artifacts at any stage of the pipeline. Because the system relies on standard text files, it remains model-agnostic and supports continuous delivery principles, where configurations are reused while generating new results for each run.
The methodology is strictly limited to sequential processes and is not designed for multi-agent cycles or systems requiring concurrent multi-user access. Debugging remains a challenge, as the current structure identifies where an error occurred but does not automatically pinpoint the specific cause within the preceding stage.
