Run Ledger
Track agent-assisted work as runs: goal, state, owner, tools, evidence, result, and next safe action.
Systems engineering for the agent era
Built for real 1000x workflows.
Durable systems for human-supervised AI operations.
Mythos Systems AI is building Mythos Control — a local Windows operator cockpit for serious AI work. It helps human operators run agent-assisted workflows with approvals, evidence, durable context, runtime visibility, and reviewable handoffs before anything risky leaves the machine.
Private preview: Summer/Fall 2026 · Local-first · Workstation path next
Why this matters
AI work needs receipts.
AI agents are becoming useful, but serious operators cannot rely on invisible work, scattered context, unclear approvals, or unrecoverable setups.
Useful AI work should be bounded, visible, approved, and reviewable.
The operator should know what ran, what changed, what evidence exists, and what still needs human judgment.
The goal is not autonomous chaos. It is human-supervised operation with context, proof, and safe handoff paths.
Mythos Control
Mythos Control is the local cockpit.
A Windows-first operator workspace for people using AI agents in real work — built around run history, approvals, evidence, evals, and support-ready handoffs.
Approvals
Keep risky or external actions behind clear human checkpoints, with context attached.
Evidence
Preserve source links, files, outputs, tests, and decisions so work can be reviewed later.
Evals & checklists
Use lane-specific checks before handoff: research, code, support packet, public artifact, workstation change.
Runtime visibility
Record which model, tool lane, and workflow path produced the result — not just the final answer.
Support-ready handoffs
Help assemble support-ready packets that explain what happened, what evidence exists, and what to do next.
Governed workstation path
AI workstations built for operators, not demos.
Mythos is moving toward a repeatable workstation model for serious operators and small teams: local-first AI workflows with documented setup, restore posture, evidence trails, and human approval boundaries.
Ready before repeatable
Known-good configuration, backup and restore posture, tool boundaries, and owner handoffs come before broader rollout.
Built to be understood later
Runs should leave enough evidence for a human operator to review what happened, what changed, and what needs attention next.
Proof, not hype
AI work that can be reviewed.
Approval before risk
Human approval before risky, external, destructive, or public-facing actions.
Evidence trails
Receipts for work performed, decisions made, and outputs delivered.
Recovery-aware workflows
Design assumes mistakes, retries, review, and rollback paths matter.
Bounded automation
Automation where it helps — with humans still in command of scope, risk, and final action.