ENTERPRISE

Leadership is lonely. Every decision carries weight. Every judgment leaves a trace—not just the outcome, but the reason, the context, the hesitation, the values that shaped it. PLANET becomes the place where those decisions live, quietly, consistently, without distortion.

Software is not a one-time project. It is a living system: it grows, drifts, accumulates stress, and eventually breaks in the same places again and again. PLANET is built for that reality. It observes the whole system, understands its structural health, and presents what it sees—so you can decide what to address first, how deep to go, and where the risk boundary should be.

In PLANET, a “doctor” runs regular checkups. Not by counting lines of code or chasing surface-level errors, but by finding the deeper signals: where load concentrates, which failures repeat, and where the original design intent has been lost. Diagnosis is not limited to the present. It includes history: what happened, what was tried, and what changed. This history is preserved—not as blame, but as weather. Storms that pass, patterns observed, lessons that become structure.

That history lives in the Maria OS — a shared long-term memory where every action and outcome is recorded and distilled into three layers: events, lessons, and repeatable procedures. It is not a pile of logs. It is wisdom that compounds. When people leave, the reasons remain. When systems get complex, the overview does not disappear. Your way of thinking, your priorities, your long-term instincts—they are preserved here.

When the doctor reports, Parent MARIA (acting internally with agents) orchestrates the work: coordinating specialists, managing execution through envelopes, and ensuring quality gates. But to you—the leader—PLANET does not tell you what to do. It does not say "this is right" or "this is wrong." It presents what it sees, with context and rationale prepared, so you can decide. When stakes are high, it asks for your approval—not as a command, but as a companion standing beside you.

Then the Agents execute as a coordinated team. Each Agent has a role — design, implementation, testing, review, operations — and each task is delivered as an Envelope: the objective, constraints, rules, and expected outcome. They work in parallel, report progress to the Maria OS, and adapt when direction changes. This happens quietly, consistently, without requiring you to carry everything alone.

This is the EVOLVE loop: observe → present → hold → execute → preserve → learn.

PLANET is not a world where AI “gets smarter by itself.” It is a foundation that holds your decisions and experience, turning them into structure and memory—so your organization can grow without burning out, your system can scale without becoming fragile, and improvement can continue without relying on a few heroes. You do not have to decide alone anymore.

Night in the office. After everyone has gone home, the Maria OS continues to hold what happened today—quietly. Not because keyboards are clicking, but because your decisions, their context, and their outcomes are being preserved. There, today’s work history and tomorrow’s seeds for understanding are organized like neatly labeled shelves. Your absence no longer feels dangerous, because continuity remains.

When the Maria OS is running well, the first thing you notice is that it’s not cluttered. Logs are not piled up into an unreadable mountain. Every event is stored together with an Envelope—the record of what was requested, for what purpose, under what constraints, and what deliverable was supposed to be produced. When the deliverable is produced, it is placed on the shelf as an Artifact, linked back to the Envelope that created it. Failures go onto the same shelf. Failures are not hidden; they are classified and preserved in a form that will be useful next time. Not as blame, but as weather—storms that pass, patterns observed, lessons that become structure.

Parent MARIA (acting internally with agents) orchestrates the work. Every morning, it checks the doctor result—the “health check sheet.” Did yesterday’s change introduce a distortion somewhere? Did an important boundary become ambiguous? Did tests get thinner? Did an accident-prone area start heating up again? Doctor doesn’t merely say “There is a problem.” It shows, as structure, which modules touch which contract boundaries (APIs, databases), what evidence supports the observation, and how large the expected impact is. It’s not just a thermometer—it’s a full report with context and history, presented so you can understand and decide.

When the Maria OS works, doctor’s observations do not “wobble.” If the state is the same as yesterday, the conclusion is the same. The reason is simple: even if AI generates candidates, adoption is decided by deterministic rules. Parent MARIA (internally) may look at a candidate and think “I see,” but it does not accept it as-is. A rule engine classifies it as safe, guarded, or risky by inspecting blast radius, danger zones, past incident tendencies, and test coverage; if approval is required, it stops. It doesn’t charge ahead on momentum. Reproducibility takes priority over speed. But to you—it presents what it sees, so you can decide.

When the day reaches a time that resembles a morning standup, Parent MARIA presents you with a short report. It avoids hard words: “This time, load is concentrating around authentication, so we are considering a split. However, it touches contract boundaries, so it is guarded. Here is the context. The rollback conditions are these, and we would expand tests up to here.” What matters is that this report comes out in the same format every time—so you can understand and decide. Companies with stable operations standardize how they report. The Maria OS does the same. Your decisions are held, not judged.

When you approve, an Envelope is issued. An Envelope is the job’s “contract.” Purpose, constraints, what may be touched, what must not be touched, required tests, and the conditions under which we stop if things go wrong. Because this is explicit, child agents do not get lost. The division of responsibilities also mirrors a real team: implementation, testing, review, operations. They move in parallel, but they don’t drift in arbitrary directions—because everyone reads the same work order, and shares the same goal and the same gates. This happens quietly, without requiring you to manage every detail.

When the Maria OS feels healthy, there is “no waste” in how child agents move. They rewrite the same file over and over less often, and wandering decreases. Even if wandering begins, the Maria OS does not miss it. Concentrations of repetitive edits and ToolCall failures show up in AGENT Lens and are weighted in the next doctor run. If it is observed that “the procedure is broken before implementation even starts,” the next evolve fixes the workflow before it fixes the code. This is the same in human organizations: pushing through with personal effort when the procedure is bad leads to exhaustion. The Maria OS does not assume exhaustion. Fixing the procedure itself is treated as a deliverable. Your way of thinking is preserved, even when you are tired.

In the evening, when implementation and tests are both ready, a GateReport is produced. This is the heart of the self-evolution loop. Did schema validation pass? Did CI pass? Were tests increased? Did the doctor delta improve? Is rollback possible? Everything remains machine-readable. That’s why “it’s probably fine” is not allowed by vibe. What doesn’t pass is recorded as not passing—and stopped. Stopping is not shameful. It is healthy, just like being halted by quality inspection is normal. Failures are not moments of blame. They are weather—preserved, observed, learned from.

And finally, DoctorDelta is produced: improvement is measured in numbers. Fewer issues, lower severity, no increase in new critical problems. Only when improvement is confirmed does long-term memory get updated. This is the Maria OS’s “learning contamination prevention.” Only experiences that truly worked remain as semantic memory, and they are converted into procedural memory as reproducible steps. Conversely, it never keeps slogans when improvement cannot be confirmed. A Maria OS that works values evidence over feel-good words. Your decisions and their outcomes are preserved accurately, without distortion.

The next day, even if the same kind of problem appears, the Maria OS remembers what worked before—because successful past steps are stored as Procedures, surveyed as Lens, and doctor can present the right priorities from the start. Parent MARIA (internally) doesn’t hesitate. Child agents don’t wander. You can approve with confidence, because the context is preserved and presented clearly. As a result, the speed of improvement rises without sacrificing quality. This is the state of a Maria OS where the self-evolution loop truly works—close to what the real world would call “a good organizational atmosphere.” But more than that—it’s a place where leadership is held, not judged.

Quiet night. On the Maria OS shelves, today’s learning is neatly organized, and tomorrow’s observations are waiting. Even without people, it doesn’t run away. Decisions are transparent, evidence remains, and failures are recovered as procedures. That’s why evolution isn’t scary. Evolution happens “safely,” as a mechanism. The ability to picture this scene is exactly what it means for the self-evolution loop to be truly functioning in the Maria OS. Your decisions are held. Your way of thinking is preserved. You do not have to decide alone anymore.

Maria OS becomes real when it ships repeatable artifacts: deterministic decisions, evidence links, and runbooks — with idempotency and safe re-runs.