DataGrout

The Unified AI Operating Layer for Agentic Intelligence

111 followers

The Unified AI Operating Layer for Agentic Intelligence

111 followers

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LLM Developer Tools

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AI Workflow Automation

DataGrout gives your AI agents memory, reliability, and cost control. Most agent frameworks fall apart at scale, context windows explode, workflows fail silently, and token costs spiral before you notice. DataGrout fixes the plumbing: connect to any system, remember across sessions, and run continuously without burning your budget. Built for teams shipping production agents, not just demos.

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DataGrout Launches

DataGrout MathDeterministic numeric tools for AI agents, zero credits

Launched on August 6th, 2026

DataGrout DataDeterministic JSON ops for AI agents, zero LLM cost

Launched on June 18th, 2026

DataGrout Frame Columnar data ops for AI agents — filter, pivot, join

Launched on June 4th, 2026

DataGrout InvariantSemantic code analysis for the AI era

Launched on May 27th, 2026

DataGrout Enterprise AI Platform for Agentic AI & MCP Integration

Launched on April 15th, 2026

Forum Threads

p/datagrout-ai

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3mo ago

We built a diff analyzer that scores whether a code change matched the stated goal

One of the core tools in Invariant is diff_analyzer.

You give it:

p/datagrout-ai

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3mo ago

Your coding agent ships code that works but does the wrong thing. How are you catching it?

We kept running into this while building DatGrout Invariant.

The agent makes a change. Tests pass. No errors. But the behaviour doesn't match what you actually asked for. No crash to debug, no obvious failure, just silent goal drift that shows up later.

We called this the "plausible but wrong" problem.

The frustrating part is that existing tools don't catch it, linters check syntax, test suites check output, but nothing checks whether the change matched the original intent.

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