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The Unreasonable Effectiveness of Separating the Task from the Model — Maxime Rivest & Isaac Miller
DSPy separates an AI task’s contract from its model, prompt, tools, and harness, letting builders swap or optimize implementations while holding behavior against the same evals.
AI Engineer · Jul 23, 2026
Source Summary
**DSPy** expresses repeatable AI work through declared **inputs and outputs**, while leaving the model, prompt, tools, and agent loop replaceable. The task is specified through instructions, enforced constraints, and examples of good behavior.
Practical Implication
Define **specs, code, and evals** before tuning an agent. A stable contract lets you compare a simple prompt, a tool-using agent, or an optimized harness without rewriting downstream integrations.
Agent-Ready Context
**DSPy** expresses repeatable AI work through declared **inputs and outputs**, while leaving the model, prompt, tools, and agent loop replaceable. The task is specified through instructions, enforced constraints, and examples of good behavior. Define **specs, code, and evals** before tuning an agent. A stable contract lets you compare a simple prompt, a tool-using agent, or an optimized harness without rewriting downstream integrations. Optimization remains bounded by how well the task and evaluation reflect reality. **DSPy Flex** can learn code or a harness, while qualitative learning from textual feedback is still presented as a research direction.
Context Map
agentcodingdata#harness-engineering#agent-evals#promptingUncertainty
Optimization remains bounded by how well the task and evaluation reflect reality. **DSPy Flex** can learn code or a harness, while qualitative learning from textual feedback is still presented as a research direction.