Welcome to DataGrout AI

Agentic Infrastructure for Autonomous Systems

DataGrout is the intelligence, governance, and coordination layer for AI agents. Connect your tools, and your agents get semantic discovery, verified multi-step workflows, policy enforcement, and cost tracking.

How It Works

You connect integrations (Salesforce, QuickBooks, SAP, any MCP server) to a DataGrout server. Your agents connect to that server through the Conduit SDK or any MCP/JSONRPC client. Between your agent and your tools, DataGrout provides:

• Discovery finds the right tools from potentially thousands based on natural language goals
• Planning builds multi-step workflows and verifies they are safe, type-compatible, and within budget
• Policy enforcement controls what agents can do: side effect limits, field-level redaction, approval gates
• Cost tracking provides estimates before execution and itemized receipts after
• Type bridging transforms data between incompatible systems via the Semio type system
• Multiplexing aggregates all your integrations behind a single endpoint
• Demultiplexing broadcasts a single request across multiple systems simultaneously

Get Started

The fastest path to working with DataGrout:

1. Quick Start – Create a server, add an integration, run your first tool call (5 minutes)
2. Core Concepts – Understand servers, integrations, tools, discovery, and policies
3. Conduit SDK – Drop-in MCP client SDK for Python, TypeScript, and Rust
4. Semio Types – How the semantic type system makes cross-system workflows deterministic

Tools

DataGrout provides a suite of tools that your agents can call directly:

• Discovery tools – Semantic search, workflow planning, guided exploration, summary overview, direct execution
• Data tools – Pure JSON/structure manipulation: get, pick, filter, sort, merge, and more
• Frame tools – Columnar data operations: filter, sort, group, pivot, join, slice
• Prism tools – Data transformation, charting, rendering, export, pagination
• Invariant tools – Semantic code analysis, structural queries, and goal alignment verification
• Logic tools – Persistent symbolic memory: store facts, query knowledge, define constraints
• Flow tools – Multi-step workflow orchestration with human-in-the-loop gates
• Warden tools – Prompt injection and adversarial content detection
• Math tools – Numeric generation, statistics, correlation, multi-model regression, normalization, outlier detection, and ranking
• Ephemerals tools – Managed view over active cached data: list datasets, inspect contents and schema
• Latent tools – Conceptual expansion: depth, breadth, and bridge modes for research and context priming
• Toolsmith tools – Forge query tools from NL goals, temper existing skills, browse the skill catalog
• Scheduler tools – Time-based and event-driven task scheduling with sensor pipeline
• Inspect tools – Execution history, details, and CTC verification

Features

• Multiplexing – Expose many integrations through one endpoint
• Demultiplexing – Broadcast one request to many systems
• Intelligent Interface – Reduce tools/list to discover and perform for simple agents
• Private Connectors – Secure access to on-premise systems without opening firewall ports
• Policy and Security – Semantic Guards, Dynamic Redaction, side effect controls
• Credits and Receipts – Cost estimation, tracking, and itemized receipts
• Cognitive Trust Certificates – Cryptographic proofs that a workflow is safe

Connectivity

• Authentication – Bearer tokens, OAuth 2.1, mutual TLS
• Tool Naming and Compatibility – Canonical, OpenAI-compatible, and transport-encoded tool name formats
• JSONRPC Transport – Full intelligence layer over plain HTTP POST
• MCP Transport – Standard Model Context Protocol

Guides

• Using the Playground – Interactive tool discovery and execution
• Using Guide Mode – Step-by-step workflow builder with skill compilation
• Sandbox – Test individual tools directly in the browser
• Interaction Settings – Configure protocols, webhooks, response payloads, and global tool arguments
• MCP Inspector – Live protocol inspection for MCP connections
• JSONRPC Inspector – Debug and test JSONRPC calls
• Agent Inspector – Telemetry, decision traces, and cognitive-level observability

Research

The DataGrout Labs papers cover the formal theory behind the platform:

• Semio: A Semantic Interface Layer for Tool-Oriented AI Systems – The type system and adapter architecture
• Cognitive Trust Certificates: Verifiable Execution Proofs for Autonomous Systems – Pre-execution formal verification
• Runtime Policy Enforcement for Autonomous AI Systems – Semantic Guards, Dynamic Redaction, side effect controls
• Credit System: Economic Primitives for Autonomous Systems – Cost tracking, BYOK, and budget controls
• Governor: Event-Driven Scheduling for Autonomous Agents – The scheduling system behind Scheduler tools

Support

• Email: support@datagrout.ai