Under the hood

The platform that makes your agents reliable and accurate

You do not need to know any of this to launch an agent — Loxtep handles it. But if you want the technical picture, here is the foundation that helps you build better agents: connected data, approved context, governance, and one source of truth your agents can reach through MCP, REST, SQL, SDK, streaming, or graph.

What makes agents reliable

The foundation better agents run on

An agentic-first data platform connects your tools, governs business context, and delivers it however your agents and apps need it — MCP, REST, SQL, SDK, streaming, or graph.

The Knowledge Foundation

Three integrated layers that make your organizational knowledge permanent, trustworthy, and useful to AI — not throwaway prompt fragments.

AI-Ready Data + Knowledge Graph

Governed, structured data assets and their interconnections — the factual foundation that AI systems read from and reason over.

How Loxtep delivers this

Governed data products (source + consumer) on a real-time streaming backbone; entity context graph; catalog discovery.

Semantics + Ontology

The meaning layer — definitions, relationships, canonical keys, namespaces, and vocabulary that let AI resolve ambiguity and speak the organization’s language.

How Loxtep delivers this

Semantic layer; lexicon and thesaurus; ontology concepts and relationships; namespace mappings; vocabulary inference from connected systems.

Skills (Procedures + Norms)

Encoded procedural knowledge (“how work gets done”) and the access norms that constrain agent behavior.

How Loxtep delivers this

Organizational Skills via the process graph; Agent-Scope Skills via scoped skill bundles; decision traces.

What Loxtep Does With It

Five capabilities that discover knowledge, manage its lifecycle, learn from usage, deliver it to any system, and keep it trustworthy.

Context Mining

AI-assisted reverse-engineering of business operations from connected systems and runtime signals, producing candidates for human review.

How Loxtep delivers this

Automated discovery from procedure inference, vocabulary inference, catalog analysis, query history, and decision traces. Exposed via loxtep_context_mining MCP tool.

Context Development Lifecycle (CDLC)

The managed lifecycle of a context artifact — draft → in_review → approved → deployed → retired — with versioning, dependency tracking, and change propagation policies.

How Loxtep delivers this

Lifecycle management via loxtep_cdlc MCP facade, extending schema versioning, workspace versioning, and lineage impact analysis with change propagation.

Compounding Learning Loops

The mechanism by which episodic experience (decision traces) is promoted, after eval/review/certification, into durable semantic or procedural memory that future agents inherit.

How Loxtep delivers this

Memory promotion from decision traces through pattern detection, review, and certification into durable organizational knowledge. Observable via the Compounding_Metric (certified_procedures_over_time).

Activation & Retrieval

The many delivery formats through which one governed knowledge layer is consumed by different systems. One source of truth, many ways to access it.

How Loxtep delivers this

MCP, REST/API, SQL/Analytics, Webhooks/Streaming, Typed SDK + CLI, and Graph/Entity Context — all from the same governed layer.

Governance & Observability

The controls that keep context trustworthy — access control, quality enforcement, lineage tracking, PII handling, and observable metrics that prove the layer is improving.

How Loxtep delivers this

Role-based access control with fail-closed enforcement; quality rules; lineage and impact analysis; PII tagging; governance flags.

Why governed context matters

Stop thinking in terms of data in a table. Think in terms of a governed data product.

A complete data product is more than the table. It's the semantic layer, lexicon, thesaurus, ontology, and process graph — plus consumption, governance, lineage, quality, and security. That's how your systems and your AI get one coherent, discoverable, governed picture.

Discovery — semantic layer and the whole picture

A table tells you columns and rows. A governed data product tells you what every field means, how it relates to other data, and how to find it — through a semantic layer agents can search instead of inventing.

  • ·Semantic layer: searchable business definitions, metrics, and artifacts
  • ·Lexicon: what things are called and what they mean
  • ·Thesaurus: how terms relate and map across systems
  • ·Ontology: how concepts connect and what depends on what
  • ·Process graph: how data is produced, linked, and traced through decisions

Consumption — how it's used

Data that can't be consumed reliably might as well not exist. Every governed data product includes consumption interfaces and contracts — so context is discoverable and usable by your apps and AI.

  • ·APIs, webhooks, and streaming interfaces
  • ·Who can access what, and under what contract
  • ·Declarative SLA and freshness metadata (consumed by monitoring, not enforced at runtime)

Governance, lineage, quality, security

AI that reasons over your data needs to know it's correct, traceable, and safe to use. Governed data products bundle governance, lineage, quality, and security so the context you feed to AI is trustworthy and auditable.

  • ·Governance: policies, access, compliance frameworks, and audit trails
  • ·Lineage: where data came from and where it goes
  • ·Quality: rules, checks, and fitness for use
  • ·Security: classification, PII, and audit trails

How Loxtep solves the context problem

Loxtep doesn't hand AI raw tables. It models and serves governed data products — with semantic layer discovery, consumption interfaces, governance, lineage, quality, and security built in. That's how we deliver the context your AI needs to reason instead of guess.

Why this is critical in the AI world

Governed context is what separates AI that reasons from AI that hallucinates.

AI needs context, not just bytes

Models that only see tables hallucinate relationships and meanings. Semantic layer, ontology, lineage, and quality give AI the structure to reason instead of guess.

Explainability requires lineage

When AI recommends an action or answers a question, you need to know where the data came from and how good it is. Lineage and quality metadata make AI outputs auditable and trustworthy.

Governance at the edge

AI that touches production data must respect access control, PII handling, and compliance. Every governed data product carries governance and security metadata — so you scale AI without scaling risk.

One substrate, many dialects

Your context, delivered how consumers need it

Loxtep doesn't force consumers onto a single interface or standard. One governed context substrate is translated into many local dialects — each linking back to the same underlying layer. Whatever consumes context in your stack, there's already an activation path.

MCP

Model Context Protocol — the hosted tool surface AI agents call directly.

Loxtep surface

mcp.loxtep.io — 19 grouped tools with OAuth 2.1 authentication

REST/API

Traditional HTTP endpoints for platform operations and integrations.

Loxtep surface

Organization-scoped API with role-based access control

SQL/Analytics

Query context and data products with SQL for reporting and ad-hoc analysis.

Loxtep surface

Execute queries, list tables, inspect schemas — all via SQL

Webhook/Streaming

Event-driven consumption — subscribe to changes as they happen.

Loxtep surface

Real-time event streams with replay and queue-based delivery

Typed SDK

Programmatic access with full type safety and IDE autocomplete.

Loxtep surface

@loxtep/sdk + CLI (init, generate, test, deploy)

Graph/Entity Context

Traverse entity relationships and query organizational knowledge as a graph.

Loxtep surface

Entity context, ontology relationships, and decision traces

Build better agents

Tools for agents and the people building them

CLI, MCP, and typed SDK on top of the platform — so you ship agents that read live context and stay inside the rules, not demos that fall apart in production.

workflows/ingest-orders.ts
// .loxtep/generated/index.ts — produced by `loxtep generate`
// Every data product and connector becomes a typed constant.
import { workspace, defineDataWorkflow, on } from '@loxtep/sdk'
export default defineDataWorkflow({
name: 'ingest-orders',
triggers: [on.connectorEvent(workspace.connectors.shopify_main)],
async handler(ctx, event) {
// Upsert into a governed data product — typed at compile time
await ctx.toolbox.upsert(
workspace.dataProducts.orders_source,
event.payload,
)
},
})

workspace.dataProducts.orders_source is generated from your instance. Accessing a field that doesn't exist fails at compile time, not in production.

Governed data products

Versioned, cataloged datasets with schema, lineage, quality rules, and consumption contracts. The unit of work is a product your org owns — not a one-off pipeline output.

Real-time streaming workflows

Ingestion and enrichment flows on an event backbone. Data moves continuously through transforms and validations — built for streaming, not batch glue code.

Semantic layer + ontology

Canonical terms, namespaces, and mappings agents can resolve. Search business definitions and artifacts instead of guessing column names from a wiki.

Data governance by design

RBAC, PII tagging, quality rules, access requests, and audit-friendly lineage enforced when you build and consume — not buried in documentation.

Catalog, lineage, discovery

Search the catalog, trace lineage impact, review evidence, and see governance flags before you ship or deprecate a data product.

AI context

Entity context, decision traces, and process intelligence wired to the platform. Agents query structured org knowledge instead of inventing relationships.

CLI lifecycle, end to end

init → attach → generate → test → deploy. Scaffold a project, link an instance, generate typed context, test locally, and deploy workflows and data products from your terminal.

Hosted MCP + scoped skills

Connect Cursor, Claude, or any MCP client over OAuth. Nineteen scoped skills teach agents the platform model — data products, governance, semantic layer — with fail-closed enforcement.

Typed SDK from your instance

loxtep generate compiles your data products, connectors, and queues into typed constants. Reference every resource by name with compile-time safety.

Why agents need a platform

Not another integration project

Most stacks help you move data. Loxtep is the agentic-first data platform — built so your agents get trusted, live business context instead of another pipeline to babysit.

Generic workflow tool
Loxtep
Pipelines as the unit of work
Data products — owned, versioned, cataloged assets with contracts and SLAs
Batch ETL / cron jobs
Event streaming — flows built for continuous, real-time data movement
Docs about data definitions
Semantic layer + ontology — canonical terms, namespaces, mappings agents can resolve
"The AI read our wiki"
AI context — entity context, decision traces, process intelligence wired to the platform
Security as an afterthought
Data governance by design — RBAC, PII tagging, quality rules, access requests, audit-friendly lineage
Opaque data sprawl
Discovery — catalog search, evidence, lineage impact, governance flags

Stop wrestling with data plumbing. Start launching agents that work.

Join the waitlist for early access. Tell Loxtep what agent you want, connect your tools, and ship an agent your business can actually rely on — without building a data engineering team.

Enterprise-grade reliability, permissions, and audit trails — without the enterprise team.

Free to join — no credit card, no commitment