Stop babysitting AI agents
Juggling worktrees. Rerunning pipelines. Babysitting agents that crash. Burning API tokens on boilerplate that Haiku could handle.
Mission Control does all of that for you.
Works with your Anthropic subscription — no API token bills.
Not a demo. This is how I ship every day — feed tasks, get PRs. Watch it work.
Opus for architecture. Sonnet for features. Haiku for boilerplate. The right model for the right task — automatically.
Lint, security scan, test coverage, architecture review — every PR passes every gate before a human sees it.
Full audit trail on every PR — task spec, agent reasoning, gates passed, review decisions. Compliance-ready out of the box.
Not mockups. Not Figma. Screenshots from the system that builds itself.
100 work items tracked across stages. Full visibility, one board.
Merge approval with confidence scores. Policy gate results. Full activity history.
541 runs. 316 successful. Real numbers from production.
Mission Control handles orchestration, isolation, policy enforcement, and evidence collection. You review the results.
YAML configs in your repo. Lint rules, test coverage thresholds, security scans, architectural boundaries. MC enforces them on every PR — no exceptions, no shortcuts.
Push task specs from your backlog. MC analyzes complexity, assigns a risk tier, and routes to the right model. Architecture gets Opus. Boilerplate gets Haiku.
Each agent works in an isolated Coder sandbox. No production access. No shared state. Temporal workflows handle retries and escalation automatically.
Every PR ships with an evidence bundle: what was requested, what the agent did, which gates passed, and why. Review the PR, not the process.
Hundreds of PRs. Full governance. Zero babysitting.
| With Mission Control | Without it | |
|---|---|---|
| Workflow | Feed tasks, get auditable PRs. One dashboard. | Juggle worktrees, switch folders, babysit each agent |
| Cost | Uses your Anthropic subscription — no per-token API bills | Burning API credits on every run, no cost control |
| Model selection | Opus for architecture, Sonnet for features, Haiku for boilerplate — automatic | One model for everything, overpaying for simple tasks |
| When agents crash | Temporal auto-retries, recovers state, escalates if needed | Lost work, manual restart, figure out what went wrong |
| Scale | Parallel agents, hundreds of PRs, zero babysitting | One agent, one task, wait for it to finish |
| Audit trail | Full evidence bundle on every PR — task spec, reasoning, gates passed | "The AI wrote it" — good luck explaining that |
CTO · 15+ years shipping production systems
I built Mission Control because I was tired of the busywork. Switching between worktrees, rerunning the same pipeline steps, babysitting agents that crash halfway through a task. I'm an architect — I want to design systems, not manage processes.
MC automates everything between "here's the task" and "here's the PR." The governance and audit trails came from my years as CTO of a regulated crypto exchange — but honestly, even if you don't need compliance, you'll want them. Knowing exactly what your AI did and why is just good engineering.
Mission Control is in private beta. Limited spots. Uses your Anthropic subscription — no API token bills on top.
For solo CTOs and small teams
Give back to the community
Don't see your question? Ask me directly.
30-minute demo. I screen-share MC running on its own codebase — real PRs, real policy gates, real evidence bundles. Not slides.
Book a demo →Or email directly: sergey@misco.dev