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Long-running agents that investigate every deploy, understand what your business cares about, remember what they’ve fixed, and only ping you when it matters.

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Benchmark · Root Cause Accuracy

Cursor + Your MCP · Opus 4.6

41%

Cursor + Foam MCP · Opus 4.6

64%

Foam · Sonnet 4.6

86%

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Built for teams at

Sound Familiar?

Challenges you are facing

Noisy alerts. So you watch dashboards yourself.

You re-tune thresholds, rewrite rules, but eventually stop trusting your alerts. So you monitor yourself.

You vibecoded a PR. Now you have to make sure it works.

Your AI code is now in production and you need to check whether it actually works for your users.

Customers find issues before your team does. And you are losing their trust.

By the time engineering knows, your customers have already created a support ticket or complained about your product.

Your observability stack is an overpriced database for your AI agents.

You are paying for glorified data storage. And you are left gluing MCPs together to build your own agents on top of it.

Scenarios

What can Foam do?

Monitor Agents

Your support agent has called get_order_status 11 times in the last 4 minutes — and failed every time.

The prompt tells the agent to pass order_id, but the tool expects orderId. The agent keeps retrying with the same malformed input, hitting the tool rate limit, and looping. 34 customers are stuck waiting. The fix is a one-line prompt change.

The Build vs. Buy Question

“We’re building in-house”

You’ll wire up Cursor with Sentry and Datadog MCPs and build an agent to do bug fixes. This agent will read logs and traces and half the time be correct. It’ll feel promising.

Then you’ll realize it only works when someone asks it to. So you’ll build a monitoring agent on top. Now you have two agents to maintain.

By week two, your agent costs are 4x what you expected. You start sweating a little. Nobody has invested time to optimize prompts or build evals to make sure it works while being token efficient. Why should they? This is not your core product.

By week six, every product change means someone has to update the agent’s context. New skills, new KPIs, features launched. So, now you go and update your agents.

By week eight, you’re still paying for your observability stack, tons of tokens, it still feels magical half the time and complete garbage the other half. And back to prompt engineering you go . . .