AgentWatch – Prevent runaway AI agents with runtime budget enforcement

Name: AgentWatch – Prevent runaway AI agents with runtime budget enforcement
Availability: InStock
Author: Mohil_Sharma

by Mohil_Sharma·Jun 28, 2026·4 points·1 comment

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AI Analysis

●●●BangerSolve My ProblemBig Brain

Pre-call budget enforcement at the edge beats Helicone's post-call monitoring.

Strengths

•Wire-protocol proxy means zero SDK—just change your base URL and combine keys.
•Session-level budgets catch runaway loops before they spiral into thousands of dollars.
•Zero data retention policy stores only metadata, not prompts or completions.

Weaknesses

•AI agent governance is crowded—Portkey, LangFuse, and Braintrust already compete here.
•Enterprise features like SOC 2 exports matter less for solo developers testing agents.

Post Description

Hi HN, I’m a solo developer and built AgentWatch to solve a problem I kept running into while building AI agents: preventing runaway loops and unexpected LLM spend before requests reach the model. AgentWatch sits in front of OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, Groq, and others to enforce budgets and runtime policies. I’d really appreciate your feedback. If you’re building AI agents, does this solve a problem you’ve experienced? I’d also love to hear what you’d improve or challenge.

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VERONICA puts an enforcement shim between your agent and the model so you can halt costly spirals before a request hits the provider — it natively exposes hard budget enforcement, circuit breakers, retry containment and degradation levels. The README + runnable runaway-loop demo make the failure mode concrete and the API (BudgetEnforcer, RuntimeContext, BudgetExceeded) is small and practical. I'd like to see richer observability/adapter docs for common agent frameworks, but as an enforcement-first primitive this is a clever, useful tool.

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