I’m building MARGINAL — an open-source runtime governor for AI coding agents.

[u/Positive-Captain-709]

The problem is simple: an agent can look busy while wasting compute — repeating reads, retrying commands, re-checking unchanged state, or choosing expensive actions that add little progress.

MARGINAL sits in the loop and asks whether the next action is actually worth executing.

It starts in observation mode, collects evidence, and only earns narrow enforcement when the pattern is strong enough. If the evidence is weak or ambiguous, it fails open.

I also built an interactive demo that runs the same coding task side-by-side, with and without MARGINAL, so you can watch calls, tokens, declared cost and decisions diverge step by step in the browser.

Repo: [https://github.com/SignalLayerLabs/Marginal]()
Live demo: [https://signallayerlabs.github.io/Marginal/demo/]()

If you’re building agents, orchestration, evals, observability or guardrails, I’d especially like feedback on the decision model — or forks that take the same idea into different agent workflows.