Wrap Any Model. Harness
Every AI Agent.
Stop hand-writing inference wrappers, integration glue, and monitoring code. DataGrout's agent harness turns any ML model or custom AI into a discoverable, governed, production-ready tool — connected to the workflows that need it.
Free to start · No credit card required
Why Operationalizing ML Models Is Still Too Hard
Getting a model to production is solved. Getting a model into agentic workflows — governed, observable, and reusable — is where teams get stuck.
Inference APIs Aren't Agent-Ready
A trained ML model exposes a raw REST endpoint. Turning that into something an AI agent can discover, invoke, and safely act on requires significant custom glue code — schema definitions, error handling, auth wrappers — all written by hand.
No Discovery Means No Reuse
When models are deployed as opaque services, other agents or teams can't find them. There's no semantic registry, no natural language discovery — just tribal knowledge about which endpoint does what.
Orchestrating Predictions Into Workflows Is Manual
A churn prediction fires — now what? Routing that output into a CRM update, a marketing campaign trigger, or a human approval gate requires hand-coded logic that breaks every time a downstream system changes.
Model Monitoring Is an Afterthought
Once a model is in production, tracking inference costs, spotting degradation, and catching runaway invocations typically requires a separate monitoring stack — disconnected from the agent workflows that drive usage.
Security and Compliance Are Not Built In
Custom model integrations expose sensitive data — customer PII, financial records — without redaction, access control, or audit trails. Security teams are left retrofitting guardrails onto something already in production.
Every New Model Requires the Same Boilerplate
Each new ML model deployed means re-implementing authentication, type mapping, logging, and policy enforcement from scratch. There's no harness that standardizes the operational wrapper across your model fleet.
The Full Agent Harness Stack
DataGrout provides every layer needed to take a model from inference endpoint to governed, observable, production-grade agent tool — without custom infrastructure.
Toolsmith.forge
Wrap any model API as a discoverable agent tool.
Toolsmith.forge creates a fully typed, semantically annotated tool from any ML model's inference endpoint or REST API — using nothing but a natural language goal description. The resulting tool is immediately searchable, invocable, and policy-governed. Forge handles EBNF grammar validation, SchemaCache for custom field awareness, and optional Cognitive Trust Certificate minting.
Learn more →Discovery
Let agents find and use your models by goal, not by name.
Once a model is registered as a tool via Toolsmith, discovery.discover makes it semantically searchable across the entire agent ecosystem. Other agents, pipelines, or developers can find your model by describing what they need — 'predict customer churn', 'classify support tickets' — without knowing the endpoint or schema. discovery.plan builds verified multi-step workflows that include your model tool.
Learn more →Flow
Orchestrate model predictions into multi-step operational workflows.
Flow operationalizes model output by connecting predictions to real-world actions. flow.into executes multi-step plans where model predictions drive conditional routing — send a churn prediction to a marketing campaign tool, trigger a human approval gate for high-risk decisions, or fan out to multiple downstream services. flow.route handles conditional dispatch based on prediction values at runtime.
Learn more →Governor
Monitor model usage, cost, and performance continuously.
Governor tracks every invocation of your harnessed model tool — credits consumed, latency, success and failure rates — without requiring separate monitoring infrastructure. Its neuro-symbolic cognition loop detects anomalies and cost spikes using zero-cost symbolic reflexes, escalating to full reasoning only when something meaningful changes. Set budget limits per model, per agent, or globally.
Learn more →Policy & Security
Enforce data guardrails around every model invocation.
Every call to your harnessed model tool passes through DataGrout's Policy engine — enforcing side-effect controls (read/write/delete), dynamic redaction of PII in inputs and outputs, and Semantic Guards that block unauthorized access patterns. PII Auto-Detection automatically masks emails, phone numbers, SSNs, and financial identifiers before they reach model inference or appear in agent responses.
Learn more →Warden
Protect model inference from adversarial inputs.
When agents pass user-supplied data to your harnessed model, Warden inspects it across three tiers before it reaches inference — canary probes detect protocol tampering, semantic intent analysis flags goal misalignment, and Prolog-backed adjudication categorizes specific threat types. Adversarial inputs are blocked before they can manipulate predictions or extract model behavior.
Learn more →Logic
Store model facts, constraints, and business rules persistently.
Logic provides the persistent symbolic memory that makes harnessed models smarter over time. Store model metadata, known prediction thresholds, or domain constraints as facts. Use logic.constrain to define business rules — 'never trigger a campaign action on predictions below 0.7 confidence' — that actively block workflow steps at runtime rather than relying on prompts the LLM might ignore.
Learn more →Cognitive Trust Certificates
Cryptographic proof that model-driven workflows ran as planned.
Every workflow that incorporates a harnessed model tool is sealed with a Cognitive Trust Certificate — a cryptographically signed attestation that the plan was type-safe, policy-compliant, and executed without deviation. CTCs provide the compliance evidence needed to demonstrate that model-driven decisions were governed, auditable, and untampered.
Learn more →From Trained Model to Governed Agent Tool
See how DataGrout closes the gap between model deployment and operational integration — without custom infrastructure.
An ML Engineer has trained a highly accurate customer churn prediction model. It's deployed as a REST endpoint. Now the team needs it integrated into the CRM and marketing automation platforms — so that when a prediction fires above a confidence threshold, the right outreach is triggered automatically, a human sales rep is looped in for high-value accounts, and the entire process is logged for compliance. Building this from scratch means writing auth wrappers, type adapters, conditional routing logic, monitoring hooks, and PII redaction — across teams, across systems.
The engineer hand-builds an API wrapper around the model endpoint. Another developer writes a scheduler to poll predictions. A third team member adds a Salesforce integration — using a different auth pattern. PII fields are redacted inconsistently. There's no way for other agents to discover the model exists. Monitoring requires a separate tool. When the model is retrained, the schema changes — and the entire integration breaks.
Toolsmith.forge wraps the model's inference API as a fully typed, semantically annotated tool — registered and discoverable across the DataGrout Hub in minutes. Discovery lets other agents find the churn model by goal. Flow orchestrates the prediction into a multi-step workflow: call the CRM, conditionally route high-value accounts to a human approval gate via flow.request-approval, and trigger the marketing campaign tool for lower-value segments. Governor monitors inference cost and flags budget overruns. Policy & Security automatically redacts PII from prediction inputs and outputs. Every workflow execution is sealed with a Cognitive Trust Certificate.
What You Get With DataGrout's Agent Harness
Stop re-implementing the same operational wrapper for every model. DataGrout standardizes it — discovery, orchestration, monitoring, security, and compliance — across your entire model fleet.
Any model becomes a discoverable agent tool
Toolsmith.forge wraps any ML inference API — REST, gRPC, or custom — into a fully typed, semantically annotated tool. Once registered, any agent on the DataGrout Hub can discover and invoke it by natural language goal, with no additional integration code.
Predictions trigger real operational workflows
Flow connects model outputs to downstream actions — CRM updates, marketing triggers, approval gates, notification systems. Conditional routing based on prediction values means the right action fires automatically, with no hand-coded orchestration logic.
Model inference is monitored and cost-controlled
Governor tracks every model invocation — latency, cost, success rate — and enforces budget limits before they're exceeded. Neuro-symbolic reflexes detect anomalous usage patterns without burning LLM tokens on routine monitoring tasks.
Sensitive data is protected at the inference boundary
Policy & Security automatically redacts PII from inputs before they reach model inference and from outputs before they return to the agent. Dynamic Redaction strategies — mask, scramble, apron — are configurable per field, per integration.
Human oversight is built into the workflow
flow.request-approval inserts a human gate before high-stakes actions driven by model predictions — high-value customer decisions, financial transactions, or irreversible operations. Agents pause and wait for explicit approval before proceeding.
Every model-driven decision is cryptographically auditable
Cognitive Trust Certificates seal every workflow that incorporates a harnessed model tool — providing signed proof that the execution was type-safe, policy-compliant, and untampered. Inspect surfaces the full decision trail for compliance and debugging.
Frequently Asked Questions
Common questions about operationalizing ML models and AI agents with DataGrout.
Ready to harness your models for production AI?
Wrap any ML model as a discoverable agent tool — with orchestration, monitoring, security, and compliance built in. Free to start.
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