Agent = Model + Harness
-
Guides (feedforward controls) - anticipate the agent’s behaviour and aim to steer it before it acts. Guides increase the probability that the agent creates good results in the first attempt
-
Sensors (feedback controls) - observe after the agent acts and help it self-correct. Particularly powerful when they produce signals that are optimised for LLM consumption, e.g. custom linter messages that include instructions for the self-correction - a positive kind of prompt injection.
-
Computational(normal coding tests) - deterministic and fast, run by the CPU. Tests, linters, type checkers, structural analysis. Run in milliseconds to seconds; results are reliable.
-
Inferential (llm in loop)- Semantic analysis, AI code review, “LLM as judge”. Typically run by a GPU or NPU. Slower and more expensive; results are more non-deterministic.