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Limits – Control layer for AI agents that take real actions

Limits – Control layer for AI agents that take real actions

by thesvp·Feb 25, 2026·9 points·2 comments
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AI Analysis

●●●BangerSolve My ProblemSlick

Wire-protocol interception means zero code changes; solves LLM control drift in production.

Strengths
  • •Deterministic enforcement (no LLM in the critical path) eliminates hallucination risk at execution boundary.
  • •Three distinct modes (conditions, evaluate, guard) cover structured rules, LLM output validation, and safety scanning simultaneously.
  • •30k+ policy checks across 16 teams proves real production adoption and validates the problem space.
Weaknesses
  • •Primarily JavaScript/Node-focused; no Python, Go, or statically-typed language SDKs mentioned, limiting enterprise adoption.
  • •Escalation queue assumes human review capacity; no guidance on scaling decision overhead in high-volume systems.
Target Audience

Backend engineers building AI agents that modify data or execute transactions

Similar To

Guardrails AI · Pydantic

Post Description

Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard.

Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement.

Three modes:

Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc

One line to integrate: npm install @limits/js

our website: https://limits.dev

our docs: https://docs.limits.dev

We've processed 30,000+ policy checks across 16 teams. Would love feedback from anyone who's built something like this internally."

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