Constraining
Type: note · Status: current · Tags: learning-theory
One of two co-equal learning mechanisms in deployed agentic systems, alongside distillation. Constraining narrows the interpretation space — reducing the range of valid interpretations an underspecified spec admits. At the light end, you add constraints: a naming convention rules out some interpretations while leaving many valid ones. At the heavy end, you commit to a single interpretation: storing a specific LLM output or extracting a deterministic function collapses the space to a point. Commitment is the extreme case of constraint — what they share is that the space gets smaller and the system becomes more predictable.
The constraining spectrum
Constraining is a gradient, not a single operation. Each step trades generality for gains in the reliability+speed+cost compound:
| Constraining | What changes | Capacity gain |
|---|---|---|
| Store an LLM output | Commit to one interpretation | One decision becomes permanent |
| Write a description field | Enable search without reading | One note becomes findable |
| Create a convention | Make future operations predictable | All operations of that kind become faster |
| Add structured sections | Enable type-specific operations | The document affords new workflows |
| Extract a deterministic function | Move from LLM to code | One operation becomes reliable, fast, free |
The last step — codification — is the far end of the spectrum where the medium itself changes (natural language → executable code). It produces the largest compound gain because it removes the LLM from the loop entirely. But it's not a separate mechanism — it's what constraining looks like when it crosses a medium boundary.
Many constraints never need to codify. A well-written description field is constrained (findable, predictable) but will never become code. A naming convention constrains agent behavior without any phase transition.
Relaxing
Relaxing — replacing a constrained component with a general-purpose one — is the reverse operation. It increases generality at the cost of the compound. When scale makes a general approach good enough on reliability+speed+cost, the bitter lesson boundary tells you to relax.
The constrain/relax cycle is a learning cycle. Each constraining step narrows the interpretation space — ruling out some of what the spec previously admitted. Each relaxing reopens it — making the system more capable for the general case. The cycle isn't maintenance — it's how the system adapts.
Relationship to distillation
Constraining and distillation are orthogonal — they operate on different dimensions of the same artifacts:
| Not distilled | Distilled | |
|---|---|---|
| Not constrained | Raw capture (text file, session notes) | Extracted but loose (draft skill, rough note) |
| Constrained | Committed but not extracted (stored output, frozen config) | Extracted AND hardened (validated skill, codified script) |
You can constrain without distilling (store an LLM output — commit to one interpretation, no extraction from reasoning). You can distil without constraining (extract a skill from notes — still natural language, still underspecified). The full compound gain comes when both operations apply.
Constraining asks: how constrained is this artifact? Distillation asks: was this artifact extracted from something larger?
Relevant Notes:
- codification — the far end of the constraining spectrum: constraining that crosses a medium boundary
- distillation — co-equal mechanism: targeted extraction shaped by use case, context budget, and agent; orthogonal to constraining
- agentic systems interpret underspecified instructions — foundation: the underspecification framework that constraining operates on
- storing LLM outputs is constraining — the simplest instance: committing to one interpretation by keeping a specific output
- methodology enforcement is constraining — applies: the instruction → skill → hook → script gradient is constraining applied to methodology
- error messages that teach are a constraining technique — instance: teaching error messages constrain interpretation space by simultaneously blocking wrong outputs and demonstrating correct ones
- deploy-time learning — the verifiability gradient across which constraining operates
- bitter lesson boundary — determines when constraining is permanent vs when relaxing is needed
- ABC: Agent Behavioral Contracts — grounds: probabilistic compliance model (p,δ,k) and Drift Bounds Theorem quantify how much drift each enforcement layer permits — formal statement of the constraining trade-off
- Harness Engineering (Lopopolo, 2026) — exemplifies: "encode standards directly into the repository" is constraining in practitioner language at production scale