> **Status: Partially Implemented** — This document describes the target evidence architecture. Items marked **Implemented** exist in the codebase; items marked **Partial** have upstream scripts but no evidence output yet; all others are **Planned**. See AGENTS.md for the current operational state.
Disinto is purpose-built for one loop: **build software, launch it, improve it, reach market fit.**
This document describes how autonomous agents will sense the world, produce evidence, and use that evidence to make decisions — from "which issue to work on next" to "is this ready to deploy."
## The Loop
```
build → measure → evidence good enough?
no → improve → build again
yes → deploy → measure in-market → evidence still good?
no → improve → build again
yes → expand
```
Every decision in this loop will be driven by evidence, not intuition. The planner will read structured evidence across all dimensions, identify the weakest one, and focus there.
> **Terminology note — "process" vs "formula":** In this document, "process" means a self-contained measurement or mutation pipeline that reads an external platform and writes structured evidence to git. This is distinct from disinto's "formulas" (`formulas/*.toml`), which are TOML issue templates that guide agents through multi-step operational work (see `AGENTS.md` § Directory layout). Processes produce evidence; formulas orchestrate agent tasks.
red-team discovers attack → new vector added to attacks/ via PR
→ evolution scores candidates against harder attacks
→ better optimizers survive
→ red-team runs again against improved candidates
```
The planner won't need to know this loop exists as a rule. It will emerge from evidence: "new attack vectors landed since last evolution run → evolution scores are stale → run evolution."
## Evidence Directory
> **Not yet created.** See harb #973 for the implementation issue.
```
evidence/
evolution/ # Run params, generation stats, best fitness, champion
red-team/ # Per-attack results, floor held/broken, ETH extracted
holdout/ # Per-scenario pass/fail, gate decision
user-test/ # Per-persona reports, friction points
resources/ # CPU, RAM, disk, container state
protocol/ # On-chain metrics from Ponder
funnel/ # Analytics conversion data (future)
```
Each file will be dated JSON. Machine-readable. Git history will show trends. The planner will diff against previous runs to detect improvement or regression.
## Delivery Pattern
Every process will follow the same delivery contract:
1.**Evidence** (metrics/reports) → committed to `evidence/` on main
2.**Artifacts** (code changes, new attack vectors, evolved programs) → PR
3.**Summary** → issue comment with key metrics and link to evidence file
## Evidence-Gated Deployment
Deployment will not be a human decision or a calendar event. It will be the natural consequence of all evidence dimensions being green:
- **Holdout:** 90% scenarios pass
- **Red-team:** Floor holds on all known attacks
- **User-test:** All personas complete journey, newcomers convert
- **Evolution:** Champion fitness above threshold
- **Protocol metrics:** ETH reserve growing, floor ratcheting up
When all dimensions pass their thresholds, deployment becomes the obvious next action. Until then, the planner will know **which dimension is weakest** and focus resources there.
## Resource Allocation
The planner will optimize resource allocation across all processes. When the box is idle, it will find the highest-value use of compute based on evidence staleness and current gaps.
Sense processes are cheap — run them freely to keep evidence fresh.
Mutation processes are expensive — run them when evidence justifies the cost.
The planner will read evidence recency and decide:
- "Red-team results are from before the VWAP fix → re-run"
- "User-tests haven't run since February → stale"
- "Evolution scored against 4 attacks but we now have 6 → outdated"
- "Box is idle, no CI running → good time for evolution"
No schedules. No hardcoded rules. The planner's judgment, informed by evidence.
## What Disinto Is Not
Disinto is not a general-purpose company operating system. It does not model arbitrary resources or business processes.
It is finely tuned for one thing: **money → software product → customer contact → knowledge → product improvement → market fit → more money.**
Every agent, process, and evidence type serves this loop.
