Intelligence Infrastructure
for Autonomous Systems
Four pillars — Foundry, Hub, Intelligence, Memory — compose into a single operating layer for your AI agents. Connect any system, plan any workflow, execute with cryptographic proof. Consume independently or together.
AI Agents Are Ready.
The Infrastructure Isn't.
Every team building with AI agents hits the same walls. Not model problems — infrastructure problems.
N×M Context Explosion
MCP servers can have 1,000+ tools. LLMs can handle ~50. Dumping the full tool list into context means agents guess blindly, pick wrong, and spiral. There's no way to search — only browse.
Workflows Fail Silently
Multi-step pipelines break on type mismatches between systems. Salesforce returns a Lead; QuickBooks expects a Customer. There's no bridge, no validation, no proof of what ran — just a silent failure.
Agents Forget Everything
Every conversation starts from zero. Business rules discovered in one session are gone in the next. There's no persistent knowledge base — just context stuffing and hallucination risk.
Continuous Inference = Continuous Burn
An agent checking "is it morning yet?" every 30 minutes burns $47 overnight. Polling-based architectures scale with time, not value delivered. Token costs compound even when nothing has changed.
Every Squad Rebuilds the Same Scaffolding
OAuth wrappers, retry logic, pagination handlers, schema mapping — written from scratch by every team, every time. Hard-won API knowledge lives in Slack threads, not reusable artifacts.
On-Prem Is a Dead End
Legacy systems, air-gapped databases, and private networks can't be reached without opening firewall ports. The choice is: expose your network or exclude your most important data.
These aren't model problems. They're infrastructure problems.
Better prompts won't fix a missing operating layer. Whether you're building on top of an enterprise data fabric or starting from scratch, DataGrout is the agentic layer your AI stack is missing.
The Platform Architecture
Each pillar solves a distinct problem. They compose into a complete cognitive architecture for autonomous agents.
FOUNDRY
DesignDefine tools & agents
Define what your agents can do. Toolsmith forges reusable, schema-aware query tools from natural language goals. Build once, callable by any agent forever.
- Toolsmith — forge query tools from natural language
- Schema-aware generation from real integration schemas
- Versioned skill catalog with provenance & IP protection
- Fork & temper existing skills without starting from scratch
HUB
ConnectAttach to real systems
Unify every tool surface behind one governed endpoint. Multiplexer aggregates servers, Private Connectors reach on-prem systems, and a 7-layer security stack protects every call.
- Multiplexer & Demultiplexer — one endpoint, any source
- MCP Primitives — 1,000+ first-party tools across Salesforce & QuickBooks
- Private Connectors — outbound-only VPN for on-prem
- 7-layer security: mTLS, policy cascade, SemanticGuard, CTCs
INTELLIGENCE
Plan & RunNeuro-symbolic execution
The planning and execution brain. Discovery finds the right tools without context explosion. Gateway builds type-safe plans. Flow validates and executes with cryptographic proof. Runtime runs it 10-100x cheaper than always-on LLM.
- Discovery — semantic search across 1,000+ tools
- Intelligence Gateway — A* planning with SemIO type safety
- Prism — natural language data transformation & type bridging
- Flow — formal workflow validation + CTCs
- Intelligence Runtime — neuro-symbolic execution engine
- Warden — multi-tier prompt injection detection
- Governor + Scheduler — continuous cognition without LLM token costs
MEMORY
ContextCurated attention windows
Agents forget everything between calls. Logic gives them persistent, queryable facts backed by Prolog — no hallucination, sub-millisecond latency. Ephemerals gives visibility into active cached data.
- Logic — persistent symbolic memory backed by Prolog
- Natural language fact storage & constraint enforcement
- logic.hydrate — goal-driven context assembly, budget-constrained
- Batteries — pre-built Prolog rule modules, install with one call
- Isolated namespaces per project, customer, or workflow
- Ephemerals — inspect & manage active cached data
More tools in the platform
Every tool below is callable via MCP — composable with the four pillars above.
Data + Frame
Deterministic JSON & columnar operations. Free — no gateway cost. Filter, sort, group, pivot, join, merge, flatten, deduplicate — all in-process, no LLM.
Math
Statistics, regression, distributions, normalization, outlier detection, and sequence generation. Free — gateway costs waived.
Conduit SDK
Production-ready MCP client in 5 languages (Python, TypeScript, Rust, Elixir, Ruby). mTLS bootstrap, OAuth 2.1, semantic discovery, and namespaced tool wrappers built in.
Invariant
Neuro-symbolic code analysis — extract structural facts, query for intent drift, diff PRs against stated goals. Multi-language via tree-sitter.
Latent
Concept expansion engine for research and context priming. Finds hidden connections across unrelated domains via depth, breadth, and bridge modes.
Tasks
Durable handles for background work. Agents check progress, wait for results, or cancel stale work without losing track of what's running.
Deliverables
Persistent work products for agents and teams. Search prior outputs, retrieve full artifacts, and register new deliverables from cached datasets.
Docs
Persistent document storage for agents. Create, update, retrieve, and list documents that survive across sessions — design docs, memos, research notes.
Batteries
Pre-built Prolog rule modules that install directly into any Logic namespace. Search the catalog, install with one call, and query predicates immediately.
Flux
Capture quality signals around agent responses and workflows. Record feedback, produce scorecards, and track how system behavior changes over time.
Intelligent Interface
A server setting that reduces tools/list to just two entry points — discover and perform. Eliminates the N×M context firehose for simpler agents.
Intelligent loop detection with consequence-aware session tracking. Blocks agent death-spirals before they happen — read, write, and destructive ops each have different thresholds.
Legacy Approach vs. DataGrout
These are real AI workflow automation and API integration examples — the same problems, solved with a fundamentally different architecture. Unlike traditional LLM orchestration frameworks, DataGrout enforces policy and type safety at the infrastructure layer. The same problems, solved with a fundamentally different architecture.
Why DataGrout Does It Differently
Most platforms compete on tool count. We treat tool catalogs as raw material — and build the operating layer on top.
We mux them instead of competing on catalogs.
Zapier and n8n become upstream inventories; we expose a single governed endpoint that includes their tool surfaces.
Single "agent surface" across everything.
One consistent tool shape, naming/versioning, auth model, logging/tracing — regardless of whether the upstream is Zapier, n8n, or a private connector.
Semantic discovery, not browsing.
Fit-checking via SemIO (IO compatibility + required keys + adapter availability) beats keyword search across thousands of actions.
Explainable selection.
Every pick includes "candidates considered / why rejected / why chosen" — instead of opaque ranking or "try something and see."
Safety lives at the decision layer.
We focus on tool choice + tool execution safety — the part agents actually break — not just API policy or connector access.
Private connectors are a core pillar, not an add-on.
Salesforce, on-prem, and private networks plug into the same discovery + approval + tracing flow as cloud SaaS.
Cross-inventory normalization.
Near-duplicate actions from multiple sources are deduped and clustered — agents see "one logical capability" instead of 12 similar actions with subtle differences.
Composable server slices per tenant or use case.
Ship curated tool bundles with guaranteed policies and contracts, instead of dumping the full upstream catalog on every agent.
Tool sprawl reduction as a product outcome.
The value isn't "more tools" — it's "fewer, better, safer tools" presented coherently.
Deterministic planning scope.
Surface reliable multi-step plans (lookup → write) with explicit gating, instead of praying for aligned general autonomy.
CTC-ready trust story.
Every deployment ships a signed manifest of what tools, policies, and rules are active — cryptographic proof of what ran, within policy, tamper-evident.
What This Means for Your Team
The platform solves different problems depending on where you sit.
Build Agents That Actually Finish Complex Work
Pain Points We Solve:
- • Agents drown in 1,000+ tool lists and guess wrong — N×M context explosion
- • Multi-step workflows fail silently on type mismatches between systems
- • Continuous agents burn LLM tokens polling for simple conditions
What You Get:
- • Discovery + Gateway: semantic tool search and A* type-safe planning — no guessing
- • Prism bridges incompatible schemas automatically; Flow catches type errors pre-execution
- • Governor splits cognition: symbolic reflexes handle ~90% of conditions at near-zero cost
Stop Rebuilding Auth, Glue Code, and Tool Definitions
Pain Points We Solve:
- • Every squad rewrites OAuth, pagination, and error handling from scratch
- • Hard-won API knowledge lives in Slack threads, not reusable artifacts
- • 'Oh no the agent wrote to prod' incidents with no audit trail
What You Get:
- • Toolsmith: forge reusable, schema-aware tools once — every agent inherits them
- • Flow validates plans before any tool fires, with tamper-evident CTCs on every run
- • Policy-aware execution prevents production accidents; kill switches work instantly
Model-Agnostic Architecture That Scales With Your Org
Pain Points We Solve:
- • Shadow LLM scripts proliferating across teams with no shared standards
- • Model vendor lock-in as teams build directly on specific LLM APIs
- • No enterprise-wide view of what agents are doing or what they cost
What You Get:
- • Single integration layer decoupled from LLM provider — switch models freely
- • Org-wide observability: full execution logs with kill switches on any agent
- • Vendor-agnostic: BYOK drops LLM markup to $0 at the platform layer
One Governed AI Fabric Across the Entire Enterprise
Pain Points We Solve:
- • No safe, governed path from agents to production and on-prem systems
- • Shadow AI across business units with no central visibility or revocation
- • Legacy and on-prem systems stranded outside the AI strategy
What You Get:
- • Single integration layer: SaaS, on-prem, and legacy under one policy stack
- • Private Connectors: reach SAP, Oracle, and air-gapped systems — zero inbound firewall rules
- • Org-wide AI spend visibility with per-team credit caps and pre-flight estimates
AI Security Architecture Your Board Can Approve
Pain Points We Solve:
- • Agents touching production systems with no audit trail or scoped credentials
- • Prompt injection is an underestimated attack surface on every agent processing untrusted input
- • Compliance requires cryptographic proof, not 'the agent was prompted to behave correctly'
What You Get:
- • Warden 3-tier prompt injection defense: canary + intent lens + Prolog adjudication
- • CTCs: Ed25519-signed proof of every workflow — before and after execution
- • Policy enforcement at the infrastructure layer: PII redaction, destructive op blocking, human approval gates
Economic Guardrails Built In
Every workflow produces a cost estimate before execution and a receipt after. No surprise bills.
Go deeper on cost optimization
How It All Works Together
Intelligence is the runtime layer everything else runs on. Foundry defines what agents can do. Hub governs how they connect. Memory grounds them across sessions.
All orchestrated via credit-based budgets and security policies
Your AI Stack Is Missing an Operating Layer.
DataGrout gives every agent, tool, and workflow a shared fabric: governed connections, intelligent planning, and cryptographically verified execution.