You've got the capital. You've got the agent running. But you're still burning hours on things your setup should handle in seconds. Bad Mutt makes capital-efficient people time-efficient.
Most AI agent deployments are held together with duct tape and hope. You followed the docs, watched the tutorials, got the thing running — and now it mostly works. But "mostly" is doing a lot of heavy lifting in that sentence.
Your API bill is higher than it should be. You're not sure why. Your agent sometimes goes dark for hours — no error, no log, no explanation. When you restart it, it's lost the thread. Every conversation starts from scratch like it has amnesia.
Unthrottled model routing, context window overflow, no caching strategy. You're sending premium model calls where a cheap one would do just fine.
Crons that stop running. Webhooks that time out quietly. No alerting, no dead man's switch. Your agent's been offline for 6 hours and you had no idea.
No persistent memory architecture. No distillation strategy. Every session starts cold. Your agent is brilliant for 20 minutes, then forgets it ever knew you.
Layers of overrides you wrote six months ago. Prompts that contradict each other. Skills pointing at dead paths. It works until one day it doesn't.
You submit your OpenClaw deployment — configs, skill files, cron jobs, prompt architecture, model routing setup. Everything Maia needs to understand how your agent is actually wired. No fluff intake form. Just the relevant files.
Maia — Bad Mutt's diagnostic AI — runs a deep review of your deployment. She's looking for cost inefficiencies, silent failure points, memory gaps, security exposure, and config conflicts. She reads configs the way a senior engineer would after a production incident.
You get a structured findings report: each issue with a severity rating, a confidence score, evidence from your actual config, and an estimated cost or reliability impact. Not vague recommendations. Specific findings with specific numbers.
Each finding comes with a fix. Step-by-step, in plain language. Prioritized by what will move the needle most. You're not left with a list of problems — you're handed a repair checklist.
After you implement, you get a follow-up review. Did the fix land? Did it surface something new? The loop closes. You don't just get a report — you get confirmation it worked.
This is pulled from an actual diagnostic report. One finding, out of typically 8–15. This one alone was responsible for the majority of the client's monthly API spend.
Your routing configuration does not define task-complexity tiers. Every request — from a simple memory lookup to a multi-step reasoning task — routes to your highest-cost model by default. There is no fallback hierarchy, no complexity classification, and no cost ceiling per request type.
# Current routing config (openclaw.json)
"model": {
"default": "anthropic/claude-opus-4", # ← premium, every call
"fallback": null, # ← no fallback defined
"routing": null # ← no task-tier logic
}
# Recommended routing config
"model": {
"default": "anthropic/claude-haiku-4", # ← cheap for routine tasks
"routing": {
"complex": "anthropic/claude-sonnet-4",
"critical": "anthropic/claude-opus-4" # ← reserved for high-stakes
}
}
60–70% of your current API spend is routing inefficiency. The majority of your agent's tasks (memory reads, simple lookups, routine skill calls) do not require premium model capacity. You're paying Opus rates for tasks Haiku handles at 95% quality.
Implement a 3-tier routing hierarchy: default to Haiku for standard tasks, Sonnet for complex reasoning, Opus only for critical decisions or explicit escalations. Estimated monthly savings: 55–65% on model API costs. Implementation time: ~30 minutes.
This isn't a course you watch alone. It's a working group — twelve people solving the same class of problem together. Daily group calls. Shared diagnostics. Real collaboration. Twelve is the number where everyone gets heard. After that, the cohort closes.
You're capital efficient. Now become time efficient. Three months of collaboration, diagnostics, and fixes — with eleven other people solving the same problems.