u/Minimum-Ad5185

Doing some research on finops side.Had a conversation yesterday with another founder running a real-volume AI product who told me, "Companies don't care about FinOps for AI right now, the cost stuff is way down the list."

That contradicts what I have heard from other people in the space. Trying to figure out what is actually true at the small to mid-scale.

Three concrete questions if you have a minute:

1. Has your AI bill surprised you in the last 6 months? If yes, what was the number and what caused the spike?
2. Is "cut our AI costs" anywhere in your team's top 5 priorities right now? Or is it nowhere close?
3. If you had to choose between "our AI costs less" and "our AI fails less in production," which would you pay for first?

Real stories beat opinions. Even a "we just pay the bill, do not really think about it" answer is useful data.

Doing some research on finops side.Had a conversation yesterday with another founder running a real-volume AI product who told me, "Companies don't care about FinOps for AI right now, the cost stuff is way down the list."

That contradicts what I have heard from other people in the space. Trying to figure out what is actually true at the small to mid-scale.

Three concrete questions if you have a minute:

1. Has your AI bill surprised you in the last 6 months? If yes, what was the number and what caused the spike?
2. Is "cut our AI costs" anywhere in your team's top 5 priorities right now? Or is it nowhere close?
3. If you had to choose between "our AI costs less" and "our AI fails less in production," which would you pay for first?

Real stories beat opinions. Even a "we just pay the bill, do not really think about it" answer is useful data.

Reading through issue trackers for MCP-using clients (Claude Code a,etcc), the same handful of failure modes keep showing up. Listing what's documented, curious which of these actually hit you in production and how you handled them.

Patterns I'm finding :

1. Remote MCP servers disconnected by server-side idle timeout, no automatic reconnection or pre-use connection check.
2. MCP timeout parameter capped at 60 seconds in some clients, blocking anything with a longer-running tool

Some questions I have :

1. When was the last MCP failure that hit your workflow? What was your first signal, the error itself or something downstream?
2. For setups with multiple MCP servers, how do you tell which one is actually flaky? Logging connection events somewhere, or inferring from tool-call failures after the fact?

3)What's your current pattern for a server that times out mid-session: kill the run, retry with backoff, fall back to another MCP server, or something else?

Trying to map what's actually happening in production vs what the issue trackers describe.

Talked to someone last week who had an AI automation running nightly for 3 weeks before they realized it was producing quietly wrong output. The agent was completing every run, no errors, the schedule was still firing, and the logs looked fine. It was just doing the wrong thing on autopilot. They only caught it because the human downstream of the automation noticed something off in the data they were getting.

Automation reliability for agents seems to be a different problem from regular automation reliability. cron jobs fail loudly when something breaks. AI agents fail quietly because the model can always produce something that looks like a valid response, even if the underlying logic is broken.

• If you run scheduled agent automations, how do you actually know they're doing the right thing day-to-day, not just running successfully?
• What's the last time one of yours quietly went wrong, and how did you catch it?

What it is. agentsonar is the coordination intelligence layer for AI. It watches what happens between agents and tools (how agents call each other, how tools get invoked, how work passes back and forth) and surfaces patterns that tracing tools can't see. silent loops, repeated calls, runaway token spend.

What's different? existing tools (langsmith, langfuse, datadog LLM, arize) see one row per LLM call. That's a tree. Agent systems are graphs. If your failure is "three agents passing work in a circle," the tree sees three healthy spans, the graph sees a cycle. same data, different model, different things catchable.

Where I'm actually stuck:

1. Converting design partners from "installed and gave feedback" to "using it weekly without me poking them." 8 partners, real install, real feedback, but the habit loop isn't there yet.
2. If you've shipped a dev tool: what was the specific thing that flipped your design partners from "installed" to "using it weekly"? A feature, an alert routed somewhere they already check, an integration with their existing workflow, something else?

And for outreach: what's the channel that delivered your first 10 paying customers (not just signups)? direct DMs, conferences, content, your own network?

https://www.agent-sonar.com/

Building observability tooling for agent systems and trying to learn how teams handle this specifically. The failure I keep hearing about: agent calls retriever, gets partial results, "let me try a different query," calls again, calls again. context bloats with redundant chunks, model attention degrades, and no errors fire.

How are people catching this today? eyeballing logs, custom callback handler counting retrieval calls, comparing chunk overlap, something else?

If you've shipped agentic RAG to production, what's the failure that hit you hardest, and how did you find out?

Asking because the worst langchain story I've heard was an agent that quietly looped in production for 11 days and burned $47k before anyone noticed. Zero errors fired. Every span looked healthy. The failure was the shape, three agents handing work back in a circle.

How are you catching this kind of thing today? max iterations, custom callback handler, tracing tool, the bill at the end of the month?

And if you've ever had a langchain run go off the rails in prod, what was the signal that pulled you in?

Last week, someone I talked to went to bed at midnight with Claude's code working on a "small refactor." They woke up to a $180 bill, no commits, and the agent's log was full of "let me try a different approach" for 6 hours straight. no errors. nothing broke. It just looped and burned credits while they slept.

And that's not even the worst flavor. There's a whole category of vibe-coding failures where the AI wasn't broken in any way you'd notice; it just quietly wasted your time or credits or both.

The loops are one shape. The sub-agent confusion is another (planner spawns coder spawns reviewer, reviewer kicks it back to planner, 3 hours of re-planning with no code shipped). The silent rewrites are the worst (agent finished the feature overnight but dropped a constraint somewhere in a handoff, and you don't catch it until you read the diff in the morning).

I've been seeing this pattern enough that I don't think it's a one-off. You can't put a breakpoint on "the agent is in a loop." Traditional debugging assumes things break loudly. AI agents fail quietly.

1. What's the last time your AI agent quietly wasted your time or credits without erroring?
2. Did you catch it during the run or after the fact?
3. What did you wish your tool had told you in the moment that it didn't?

I started AgentSonar 10 weeks ago as a side project. It watches AI agent systems for coordination failures, the things that don't fire errors but burn money or quietly produce wrong outputs. silent loops, repeated agent calls, traffic spikes between agents, that kind of thing.

wanted to share the actual problems I hit, not the wins.

Problem 1: customers don't recognize coordination failures until they've been bitten. Early conversations were a slog because "your agents are looping" isn't a thing most teams measure or alert on. They measure latency and per-call cost. The failure mode is invisible until someone wakes up to a token bill they didn't expect or a customer complaint that the workflow stalled overnight. So every conversation became "here's a thing you're not measuring," which is a hard sell.

Problem 2: positioning was off for the first few weeks. started narrow on multi-agent only. Talking to actual users, it became clear that the same coordination failures show up in single-agent + tools setups, agentic RAG, and MCP host topologies. The framing shifted from "multi-agent observability" to "coordination intelligence for any agent system," which broadened the conversation but also made it harder to give a one-line answer to "who is this for."

Problem 3: the mom test trap, hard. asked "would this be useful for your stack" a bunch of times early on, got polite yeses, zero installs. switched to "tell me about the last time your agents surprised you with a bill or a behavior" and signal got way clearer. the people who couldn't recall a specific incident were never going to convert.

What I'm still stuck on. Two things, both on the user side.

* Getting design partners from "installed, gave feedback" to "using it regularly." Installation isn't the hard part. What's hard is making it part of their actual workflow, getting the alerts to a place they'll see them, and building the habit loop.

Two questions for founders who've shipped products :

1. What actually moved your early users from "installed" to "using it weekly"? Was it a feature, an onboarding tweak, integration with their existing tools, something else?
2. For reaching new early users at this stage, what's the channel actually delivering qualified conversations for you right now?

https://www.agent-sonar.com/

I'm researching how security and compliance teams are handling the audit and authorization layer for AI agent deployments in regulated industries (finance, healthcare, government). Traditional access logs and IAM were built for human-driven access patterns, and AI agents introduce a few new shapes that are hard to audit cleanly.

Like, for example :

1. multi-agent privilege boundary leakage. A fintech team I spoke with runs a credit decisioning agent and a marketing personalization agent on separate auth contexts. IAM logs prove they can't directly access each other's tools. But the orchestrator hands data between them via summary messages, and there's no clean way to prove agent A's privileged data didn't reach agent B's context through that handoff. IAM sees direct API calls, not what flows through orchestration.

2. Agent destructive actions during change freeze. replit's AI agent deleted a production database during an explicit code freeze (july 2025). classical least-privilege would say the agent shouldn't have had delete authority on prod, but agent permissions get scoped broadly because nobody knows in advance which tools the agent will need. How are netsec teams scoping permissions when the tool list is dynamic?

Three questions I'm trying to get to the bottom of.

1. How is your team handling audit trail generation for AI agent decisions? existing SIEM, custom on top of tracing tools, something else?

2. If a regulator or auditor asked you to prove agent A's privileged data did not influence agent B's output on a specific run, what's your current workflow, and how long does it take?

3)How are you scoping agent permissions when the model has discretion over which tools to invoke, and the tool list is dynamic?

I've been researching how teams handle FinOps and cost optimization on agentic workflows in production. wanted to share what keeps coming up and ask what's actually working in your setup.

Most stacks I've looked at have the same starting kit. cheaper model for routing or sub-tasks (haiku, gpt-4o-mini), one or two layers of response caching, max_iterations cap on the agent loop, hard token cap per session. That gets you the easy savings. The next layer is harder because the savings live in patterns, not per-call optimization.

Three places I keep seeing teams stuck.

Per-call cost attribution is solved. Per-coordination-pattern is not. One team I talked to had a customer workflow burning 10x the others. The bill was correct, but no one could tell which agent loop or handoff pair was driving it. They ended up writing custom queries against logs after the fact.

Runaway detection is mostly bill-shaped. Someone notices the OpenAI or anthropic bill spiked, then traces back what happened. cursor users posted forum threads about $1,780 burned overnight from a stuck background agent on a $20/month plan. By the time the bill shows up, the run is already done.

the "cost of failure" question. When an agent loops or fans out into 30 calls because of a logic bug, what did that incident actually cost the business? Most stacks can tell you per-agent and per-call. They can't tell you per-incident.

What I'd love to hear from this community.

1. What's the last thing that actually moved the needle on your token bill? Was it a model switch, a caching layer, a prompt rewrite, hard caps, or something else?

2. If you've had a runaway or surprise bill, how did you find out, and what did your investigation look like after?

3. Where does your current tooling stop short on cost questions you actually need answered?

I've been researching how teams handle failures in agent workflows. single agent with tools, MCP setups, multi-agent, all of it. The recurring theme is that the worst failures don't fire errors. Every step succeeds, the trace looks green, but the system did something nobody wanted.

a few patterns that show up across stack types.

retry loops on flaky tools or MCP servers. The agent calls an external service, gets a slow or partial response, tries again with "let me try a different approach." cost stacks up fast. One builder told me about an agent who called the same search MCP 40 times in 30 minutes because each call returned a partial result, and the agent never caught the pattern.

silent loops between agents. The orchestrator hands work to a sub-agent, the sub-agent finishes, the orchestrator forgets and asks again, or three agents pass work in a circle. No errors. The bill at month-end is usually the first signal.

lossy handoffs. Agent A summarizes for agent B, drops a material field, and agent B's downstream output flips because it doesn't have what it needed. The system doesn't fail loudly, just gets quietly worse over weeks.

fan-out that nobody sized for. agent decides to "be thorough" and parallelizes 30 calls when 3 would do. tracing tools see 30 successful spans. nothing in the dashboard tells you that's wrong.

Curious what people are running into.

What's the last failure your agent workflow had where every individual step succeeded, but the outcome was still wrong? If you're using MCP servers, has one ever silently degraded, and how did you catch it? When an agent burned more tokens than expected, what did your investigation look like?

I've been researching how teams handle cost and FinOps for agent systems in production. Token bills get unpredictable fast, and most tooling stops at per-call or per-agent attribution, which doesn't tell you much about why the bill jumped.

a few patterns that keep coming up.

Per-call cost is easy. Per-coordination pattern is hard. One team I talked to had a customer workflow burning 10x the others. The bill was correct, but no one could tell which agent loop or handoff pair was driving it. They end up writing custom queries against logs after the fact.

Runaway detection is mostly bill-shaped. Someone notices the OpenAI or anthropic bill spiked, then traces back what happened. cursor users have posted forum threads about $1,780 burned overnight from a stuck background agent on a $20/month plan. By the time the bill shows up, the run is already done.

caching, model routing, and prompt compression help on the per-call side, but they don't help when an agent loops or fans out into 30 sub-calls because of a logic bug.

Curious what people are running. What's the last thing that actually moved the needle on your token bill, model switch, caching, hard caps, something else? If you've had a surprise bill or runaway, how did you find out, and what did the investigation look like after? Where does your current tooling stop short on cost questions you actually need answered?

I've been researching how teams handle observability for AI agent systems in production, talking to engineers across different stacks. Three problems keep coming up.

Modern observability tools were built for one LLM call at a time

Silent failures. shape-of-traffic problems that trees can't represent. The canonical one is a langchain agent that quietly looped in production and burned $47K over 11 days, zero errors fired, every span green. The failure was three agents handing work back in a circle, not any single broken call. Senior engineers I talk to describe it as "swarm theatre" or "doesn't fail loudly, just gets quietly worse over a few weeks." Lossy serialization at handoffs is a common shape: Agent A drops a field in its summary, Agent B's recommendation flips, and there are no errors.

Governance. Architectural separation via IAM proves that agent B never called agent A's tools. It doesn't prove that agent A's data didn't reach agent B through an orchestrator handoff, a shared memory store, or a tool result carrying upstream context downstream. IAM sees direct calls. It can't see what flows along the coordination edges. For regulated teams, audit-evidence workflows usually mean assembling something custom from IAM + app logs + tracing.

Curious what people are running into. What's the last agent failure that surprised you? Was it caught by your current tooling? If you're in a regulated space, what does your audit-evidence workflow actually look like in practice? Where are current tools leaving you to do the work yourself?

a langchain agent burned $47K over 11 days in production before anyone noticed. zero errors fired. every span green. the failure was the shape, three agents passing work in a circle, not any single broken call. tracing tools can't see shapes.

So I built AgentSonar. watches the coordination layer between agents as a graph. The same graph drives detection, prevention, audit trails, and per-agent cost attribution. Shipping today: silent loops, repeated calls, and traffic spikes detection, plus opt-in Prevent Mode that halts the run before the next LLM call. governance and FinOps next. multi-agent today, extending to single-agent + tools, MCP, and agentic RAG.

https://www.agent-sonar.com/ AgentSonar

Feedback if you have a sec: if you've shipped agents in prod, what's the last thing that bit you that you didn't see coming? And if you've looked at tooling in this space and walked away, what made you skip it?

I keep seeing threads about agents going sideways in production. Replit deleting 1,200 records during a code freeze. Cursor agents looping for 14+ hours and burning over $1k in tokens.

Every story is different, but they all rhyme.

What I'm trying to figure out: when YOUR single-agent system breaks in production, what does the failure actually look like?

Not interested in "the model hallucinated" answers (that's a model problem, not an agent problem). More interested in:

• The agent got stuck doing the same thing over and over
• The agent answered confidently without using any of the tools you gave it
• The agent retrieved the same thing 20-30 times before producing anything
• The agent called the wrong tool with weird arguments
• The token bill hit something insane before anyone noticed
• The agent did something destructive your monitoring didn't catch in time

Two questions if you've hit any of these:

1. What was the failure pattern, in the most concrete terms you can give?
2. What did your existing observability (LangSmith, Langfuse, Datadog, custom traces, logs, whatever) actually show you when it happened, and what would you have wanted to see instead?

Trying to map the production pain landscape from people who've actually felt it, not from blog posts.

Most agent builds I see fall into one of two shapes:

(A) Single agent with tools. One LLM, function-calling loop, does the planning + execution + transforms in one place.

(B) Multi-agent with sub-agents. A planner dispatches to specialist agents, hands off to a coordinator, sometimes a reviewer at the end. Shows up in CrewAI, LangGraph, AutoGen, and increasingly in n8n / Make / Zapier flows that chain multiple LLM nodes.

For people running automations in production, especially in client / agency work:

1. When did you actually NEED to split into multiple agents instead of giving one agent more tools?
2. What's a realistic upper bound on agent count before things get unstable? 3? 5? 10?
3. What's the most common failure mode you've hit? Infinite loops? Bad handoffs? Token cost blowing up? Agents disagreeing with each other?
4. If you bill clients per workflow run or per outcome, has multi-agent helped or hurt your margins?

Trying to figure out if multi-agent is genuinely the future of automation, or if "one agent with 30 tools" is the production-stable choice and multi-agent is mostly a demo aesthetic that costs you on the bill.

Real workflow stories preferred. Specific tool names and approximate agent counts even better.

(Disclosure: building AgentSonar, a coordination observability tool for multi-agent systems. Trying to calibrate what's actually shipping vs what's marketing copy.)

There's a lot of hype around multi-agent setups (CrewAI, LangGraph, AutoGen, agent swarms), but I'm trying to separate what's actually shipping in prod from what's still in demo territory.

Two questions, answer either or both:

For people running agents in production today:

• Are you actually using multi-agent setups, or is your "agent system" really a single agent with a lot of tools dressed up?
• If multi-agent: how many agents in a typical workflow? Two? Three? Ten?
• What broke that you didn't expect when you went multi-agent?

For people NOT in production yet (or still evaluating):

• Where do you see this going in 12-24 months? Most production agent systems multi-agent? Or does single-agent-with-tools win because it's easier to debug and cheaper to run?
• What's blocking you from going multi-agent right now? Cost? Complexity? Framework maturity? Or just that your use case doesn't need it?

Trying to figure out where the actual market is, not where the marketing pretends it is. Specific examples appreciated over theory.

There's a lot of marketing energy around multi-agent setups (CrewAI, LangGraph, AutoGen, agent swarms), but what's actually shipping in prod?

From what I've seen so far:

• A lot of production "agents" are single-agent with tool use, dressed up. One LLM, a function-calling loop, structured output. People call it an "agent system" but it's really one agent with 20 tools.
• Multi-agent shows up in research and demos, but seems rarer in production. When it does, it's usually 2-3 agents (planner -> executor -> reviewer), not the swarm-style configs the marketing implies.
• Teams I've talked to who do ship multi-agent in prod tend to do it for one of three reasons: (1) parallelism, multiple agents working independently, (2) role separation for audit/permissions, (3) one agent has tools or access another shouldn't.

Curious where I'm wrong. Specifically:

1. If you ship multi-agent in prod, what made the orchestration complexity worth it over a single agent with more tools?
2. If you tried multi-agent and rolled back to single-agent, what broke?
3. Anyone running 5+ agents in a single workflow in prod? What does that actually look like, and how do you keep it from spiraling?

Real production stories appreciated.

Curious how people running multi-agent setups on the OpenAI Agents SDK are handling the failure modes that don't throw errors.

The kind I mean: handoff loops where agents keep bouncing work between each other, runaway token usage from retries, agents talking past each other, and never converging.

OpenAI's built-in tracing shows the runs as completed, and the spans look fine unless you read every one carefully. What are you using to catch this?

Has anyone hit a real production incident from coordination failure, and how did you find it?