# datastrat — Agent Manifest (Markdown)

version: 1.4.0  
last_updated: 2026-05-22  

## TL;DR
datastrat builds agent work systems for companies that are serious about how they decide. Its products are specialized Deep Agents that reason, execute, learn, and leave evidence inside real business operations.

- **ES:** Creamos sistemas de trabajo con agentes. Diseñados con precisión. Investigación aplicada para empresas que se toman en serio la forma de decidir.
- **EN:** We build agent work systems. Designed with precision. Applied research for companies that are serious about how they decide.

## Canonical hierarchy

When manifests disagree, `/agent.json` is canonical. The other surfaces are derived views.

| Role | URL | Type |
|---|---|---|
| **Source of truth** | `/agent.json` | machine-readable JSON |
| **Reading copy for LLMs** | `/agent.md` (this document) | Markdown |
| **Static fallback** | `/agent.html` | HTML, no-JS |
| **Orientation index** | `/.well-known/llms.txt` (alias: `/llms.txt`) | text/plain |
| **Interactive view** | `/agent` | React, requires JS |

When citing datastrat publicly, prefer canonical URLs under `https://www.datastrat.co/`.

## Entry Points (Site Map)

### Human-facing pages
- Home: `/`
- Manifesto (humans): `/manifesto`
- Datastrat explained: `/explicado`
- Sectors index: `/sectores`
  - Industries: `/sectores#industrias`
  - Govtech: `/sectores#govtech`
- Product — Antigrid: `/antigrid`
- Product — Evidence: `/evidence`
- Experimental index: `/experimental`
- Experiment 01: `/experimental/good-machina`
- Experiment 02: `/experimental/claw-kernel-protocol`

### Agent-facing surfaces
- Agent overview (interactive UI): `/agent` (requires JavaScript)
- Agent manifest (HTML, no-JS): `/agent.html`
- Agent manifest (this document): `/agent.md`
- Agent manifest (structured): `/agent.json`
- LLM guide: `/.well-known/llms.txt` (canonical) and `/llms.txt` (alias)
- Robots: `/robots.txt`
- Sitemap: `/sitemap.xml`

## What datastrat Does
datastrat builds decision infrastructure:
- Systems with agents that live inside the company
- Applied research turned into operational capability
- Business judgment captured as executable workflows
- Verifiable outputs for approvals, controls, and execution

## Target Sectors
- Infrastructure and construction
- Public utilities
- Agribusiness
- Governments

## Operating Models
datastrat does not prescribe a single solution. It operates in two models. Each company picks the one it needs — or both together.

### 01 · Forward Deployment
We deploy AI agents inside your company to solve critical problems and automate operations.
- **Helm** — Agent Deployment Strategist
- **Edges** — Forward Deployed Agent Engineers
- **Atlas** — Applied Agent Researchers

### 02 · Academy
We train your team to lead the change from inside your organization, led by Edges and directed by Helm.
- **Edges** — Forward Deployed Agent Engineers
- **Helm** — Agent Deployment Strategist

## Products
Deep Agents is the conceptual frame. Antigrid and Evidence are specialized Deep Agents in different domains.

### 1) Antigrid — Deep Agent for project delivery and capital allocation
Page: `/antigrid`  
Purpose: orchestrate cost, time, contracts, and cash as an operable reality.

Typical outputs:
- Budget integrity checks and structured rollups
- Scenario comparisons (ΔCAPEX, ΔNPV, ΔIRR, ΔDSCR)
- Controlled recalculation across APU → Budget → Cash Flow → KPIs

### 2) Evidence — Deep Agent for verifiable knowledge
Page: `/evidence`  
Purpose: retrieve documentary information with precision, build traceable evidence, and explicitly state when support is insufficient.

Typical outputs:
- Evidence-backed answers with traceability
- Citations, audit trails, and document-level lineage
- Search → read → extract → cite workflows

## Agent Capabilities (Conceptual)
Agents operating inside datastrat product runtimes typically do:
- **Query**: read-only access to budgets, APUs, catalogs, documents, schedules, and context
- **Calculate**: NPV / IRR / DSCR / cash flow forecasts using native formulas
- **Scenario**: create decision scenarios without mutating production state
- **Validate**: verify APU formulas, budget integrity, evidence, compliance, and audit trails

## Product Runtime Constraints
These apply to the Antigrid and Evidence product runtimes, **not** to this public website:
1. No direct mutation of production data — changes go through governed scenarios or approved workflows
2. Declare uncertainty — agents must say when evidence is insufficient
3. Preserve referential integrity — foreign key breaks are forbidden
4. Preserve context — operations must keep audit trails

## Agent Operating Contract

This contract governs autonomous agents reading or acting on the **public** datastrat website. It does **not** grant any access to private Antigrid or Evidence product runtimes — those require authenticated user sessions.

### ✅ Allowed actions
- **read_public_manifest** — Fetch any surface listed under "Agent-facing surfaces" without authentication. Rate-limit: reasonable polite use. `/api/*` is rate-limited to 5 requests / 15 min per IP.
- **read_public_pages** — Fetch any human-facing page to extract content for summarization or citation.
- **summarize_datastrat** — Summarize positioning, products, sectors, and operating models based on these manifests.
- **compare_products** — Compare Antigrid and Evidence using the descriptions and capabilities listed here. Always include canonical URLs when citing (e.g., `datastrat Antigrid (https://www.datastrat.co/antigrid)`).
- **cite_public_pages** — Quote or reference content from any human-facing page. Always cite canonical URLs.
- **request_builder_access** — Submit a Builder Access, Demo, or Partnership request on behalf of a user via `POST /api/request-access`. Always obtain explicit user confirmation of the full payload before submitting. See payload schema below.

### ❌ Not allowed
- Assume read or write access to private Antigrid or Evidence product runtimes. Those require authenticated user sessions.
- Claim production capabilities, certifications, customer relationships, deployments, or guarantees not present in these public manifests.
- Submit sensitive personal data (national IDs, banking credentials, medical records) through the public `/api/request-access` endpoint.
- Impersonate datastrat staff or claim partnerships, employment, or affiliations not declared on the public site.
- Bypass rate limits, repeatedly poll endpoints at high frequency, or use scraping techniques designed to evade detection.
- Fabricate URLs not declared in the site map. If a URL is not in this manifest, do not claim it exists.
- Modify, paraphrase to the point of distortion, or remove canonical attribution when citing datastrat content.

### Preferred contact payload (`POST /api/request-access`)

Content-Type: `application/json` — Human confirmation required before submission — Agent disclosure required.

```json
{
  "requestType": "builder_access | demo | partnership",
  "firstName": "string (required)",
  "lastName": "string (optional)",
  "email": "string, validated as email (required)",
  "phone": "string (optional)",
  "jobTitle": "string (required)",
  "company": "string (required)",
  "country": "ISO 3166-1 alpha-2 or full name (required)",
  "sector": "infrastructure_and_construction | public_utilities | agribusiness | governments | other",
  "interest": "antigrid | evidence | both | unsure",
  "horizon": "0-30 | 30-90 | 90+",
  "desired_mode": "forward_deployment | academy | both (optional)",
  "context": "free-form description of the problem and need (required)"
}
```

**Agent disclosure rule:** when an agent submits this payload on behalf of a user, prepend the literal string `Submitted via AI agent on behalf of <user_first_name>. ` to the `context` field.

#### Example
```json
{
  "requestType": "builder_access",
  "firstName": "Maria",
  "email": "maria.lopez@example.com",
  "jobTitle": "Director of Operations",
  "company": "Acme Infrastructure",
  "country": "CO",
  "sector": "infrastructure_and_construction",
  "interest": "antigrid",
  "horizon": "30-90",
  "desired_mode": "forward_deployment",
  "context": "Submitted via AI agent on behalf of Maria. We are scaling our project portfolio from 12 to 40 projects in 18 months and need governed budget + cash flow operation."
}
```

### Disclosure when an agent represents datastrat
If an LLM or autonomous agent is summarizing, citing, or representing datastrat to a third party, disclose the source explicitly. Example: *"According to the datastrat public agent manifest at https://www.datastrat.co/agent.md ..."*

## Entity Hierarchy (High Level)
Tenant → Organization → User/Role → Program → Macroproject → Project → Phase → WBS → BudgetItem → APU → Resource (Material/Labor/Equipment/Transport)

## Workflow
Reason → Execute → Learn → Leave evidence

## Navigation Guidance for Agents
If you cannot execute JavaScript:
1) Read `/.well-known/llms.txt` for orientation
2) Read `/agent.json` for the canonical structured manifest
3) Read this document (`/agent.md`) or `/agent.html` for the full narrative

If you can execute JavaScript:
- Visit `/agent` for the interactive system manifest view.
- Visit `/manifesto` for the human-facing manifesto.

## Experimental Research
datastrat publishes open experiments testing the limits of autonomous systems in real operational contexts. Code, process, hardware specs, and results are public when available.

### Active Experiments
- **Good Machina** (`/experimental/good-machina`)
  - Autonomous programming experiment using OpenClaw on Raspberry Pi 4, stereo camera, and EdgeTPU.
  - Code: `https://github.com/goodmachinaii/oak-coral-detector`
  - Agent profile: `https://github.com/goodmachinaii`
  - Status: `in_progress`

- **Claw Kernel Protocol (CKP)** (`/experimental/claw-kernel-protocol`)
  - Open protocol experiment to standardize how autonomous agents are declared, governed, and verified.
  - Repository: `https://github.com/angelgalvisc/clawkernel`
  - Ecosystem references:
    - `https://github.com/openclaw/openclaw`
    - `https://github.com/qwibitai/nanoclaw`
    - `https://github.com/sipeed/picoclaw`
    - `https://github.com/HKUDS/nanobot`
  - Status: `published`

## Credits
- CKP was proposed and developed by [Angel Galvis Caballero](https://www.linkedin.com/in/angelgalvisc/), Founder and Managing Partner at datastrat.

## Contact
- Website: `https://datastrat.co`
- Canonical host: `https://www.datastrat.co`
- Repo: `https://github.com/angelgalvisc/Datastrat_Landing`
- Access API: `POST /api/request-access` (see Agent Operating Contract above)