Most companies do not have an AI adoption problem anymore. They have an AI systems problem. Tools, prompts, data and workflows are scattered everywhere, and the owner still pays with attention for every handoff. We are building Crankflo to turn that mess into one operating system: goals, Todo, Project Memory, roles, access, proof, automation and code. The point is not to add one more AI tool. The point is to make the company itself programmable and visible. A goal becomes work. Work gets the right executor. The result is checked. The lesson becomes a rule. The stable rule becomes automation. Curious what people here think: which process in your team is still held together by tabs, messages and memory?
While building WAIL, one problem kept coming up: infrastructure-level failures are easy to recognize when a provider returns a timeout or 5xx. The harder cases are requests that technically succeed.
A request can return 200 OK while first-token latency has increased significantly, throughput has degraded, or the stream has become unstable. From the application's perspective the call succeeded, but operationally the execution may already be unhealthy.
The approach I ended up taking with WAIL was to establish runtime baselines, evaluate degradation while execution is happening, and separate observation from control. Depending on the situation, WAIL can observe, retry, or reroute, while recording signed evidence of the decision and outcome. It runs alongside the provider client rather than requiring a gateway.
I'm curious how other teams running AI systems in production draw this line:
A month ago this was just an invoice that annoyed me. I launched it here yesterday.
Five people on Google Workspace, $420 for the year. Two of them sent maybe six emails a month between them, and one was a shared address that existed so a contact form had somewhere to post. Full seat price for what was basically a database row with an MX record pointed at it.
The thing that bothered me is that a mail server doesn't care how many people are on it. It cares about bytes stored and bytes moved. Everyone bills the human and pays for the gigabyte, which is why adding careers@ or invoices@ costs you money it really shouldn't.
So I built one that charges for the storage and gives the headcount away. $9 a year, unlimited mailboxes, unlimited domains, 10 GB pooled across the whole account, $0.40/GB after that. One plan, no tiers, no free version, no per-seat math.
Traditional cloud hosting was designed for a human-first workflow: a developer writes code locally, commits to Git, triggers a multi-minute CI/CD build, and deploys to production once or twice a day.
As software development shifts from human-only coding to AI-assisted and autonomous agent loops, that infrastructure model starts breaking down.
I've tried agent orchestrators like Conductor but I've never really stuck to it long term because I have no way of verifying the huge amount of work they've done with basically no validation. I always end up going to my one-agent Claude Code workflow and always feel like I'm missing out.
What kept happening to me:
3-5 agents fan out, each opens a PR
CIs pass, diffs look reasonable
I can't possibly click through every preview by morning
I merge based on the diff and CI signal
Something breaks in prod the next day because the agent did what I asked literally, but the feature doesn't REALLY work
