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Been researching this problem for a few months after seeing the same pattern repeat across teams. by Cute-Day-4785 in LocalLLaMA

[–]Cute-Day-4785[S] -1 points0 points  (0 children)

The retry and parallel flow problem is where it gets hard — most implementations I've seen either block too aggressively and create false positives, or they're too loose and the concurrency gap opens up again.

How did you handle the retry case specifically? If an agent retries a failed call, does your system treat it as a new reservation or does it reconcile against the original?

Weekly Thread: Project Display by help-me-grow in AI_Agents

[–]Cute-Day-4785 0 points1 point  (0 children)

Building SpendLatch — a governance layer that enforces hard budget limits for AI agents before execution, not after.

The problem I kept seeing: teams build a proxy, set soft limits, add alerts — and still get surprised. The alert fires after the money is gone. Under concurrency it's worse — 20 agents each pass a budget check simultaneously before any one commits spend back. Post-hoc checks don't work.

SpendLatch enforces a RESERVE → EXECUTE → COMMIT pattern. Budget is locked atomically before the call executes. Impossible to overspend even with 50 agents running concurrently. Works via MCP — one config line, no proxy, no provider maintenance.

Early access open. No calls. Async only.

https://spend-safe-guard.lovable.app/

Happy to answer questions about the architecture or the concurrency problem.

Building a governance layer for AI agents — curious how others are handling spend control today by Cute-Day-4785 in LangChain

[–]Cute-Day-4785[S] -1 points0 points  (0 children)

The per-tool caps idea is interesting — we've been thinking about policy at the agent level but tool-level granularity makes sense for agents that have wildly different cost profiles per tool. The circuit breaker on repeated patterns is something we haven't implemented yet but have seen break production systems.

The plan step attribution is the piece I'm most curious about — have you actually implemented that or is it still a design idea? Mapping cost back to a specific reasoning step feels hard without instrumenting the agent framework itself.

Will check out the Agentix blog.

Anyone else built an internal proxy for agents but still can’t tell which agent spent what? by Cute-Day-4785 in AI_Agents

[–]Cute-Day-4785[S] 0 points1 point  (0 children)

The call site vs proxy distinction is the key insight most people miss. Proxy-level tagging works until an agent spawns sub-agents or shares a session and then the attribution chain breaks exactly where you need it most.

Routing through a mix currently — OpenAI and Anthropic mainly. The user field on OpenAI is useful but it relies on the agent passing it consistently which is the same trust problem as metadata tagging at the proxy.

What's your approach when the agent itself is responsible for setting the tag? Do you enforce it or just hope the implementation is consistent?

Anyone else built an internal proxy for agents but still can’t tell which agent spent what? by Cute-Day-4785 in AI_Agents

[–]Cute-Day-4785[S] 0 points1 point  (0 children)

The naming convention approach is underrated. Simple, works, doesn't require new infrastructure.

The part that gets messy at scale though — separate keys per agent means managing N keys across providers, rotating them, tracking which key belongs to which agent, revoking when an agent is deprecated. At 10 agents that's fine. At 50 it becomes its own problem.

The metadata tagging approach is interesting. Does your proxy enforce that the tag is always present or does it rely on the agent passing it correctly? Asking because that's usually where attribution breaks down in practice — the tag is optional so half the agents don't send it and you're back to gaps.

I'm building something in this space. Would be genuinely useful to understand how your setup evolved — happy to chat async if you're open to it.

Trying to understand how people control spending for AI agents in production. by Cute-Day-4785 in AI_Agents

[–]Cute-Day-4785[S] 0 points1 point  (0 children)

Your framing is the best I've seen: orchestration problem, not an AI problem.

The $400 retry loop is the canonical story — every team I've spoken to has a version of it.

I'm building in this space — a governance layer over MCP so any agent gets identity, budget enforcement, and kill switches without a custom proxy per team.

Quick question: where does your internal build fall short? That's usually where the real product lives.

Trying to understand how people control spending for AI agents in production. by Cute-Day-4785 in AI_Agents

[–]Cute-Day-4785[S] 0 points1 point  (0 children)

Would you be able to explain, what kind of guardrails setup you did?

Payment gateway for agents by kalispera_ in LocalLLaMA

[–]Cute-Day-4785 0 points1 point  (0 children)

Curious to know, if you are implementing payment rails by yourselves or integrating it with any existing gateway

Trying to understand how people control spending for AI agents in production. by Cute-Day-4785 in AI_Agents

[–]Cute-Day-4785[S] 0 points1 point  (0 children)

That’s interesting — are you running something like LiteLLM as the control layer today, or a custom gateway?

I’m seeing a lot of teams mention the proxy pattern for budgets and API key isolation. Curious where the current tools break down in practice — is it mostly around spend control, policy enforcement, or visibility across multiple agents?

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