AI Agent Development
Tools for Production Agents
DataGrout gives developers a complete infrastructure layer for building sophisticated AI agents — persistent memory, multi-step orchestration, semantic code analysis, injection defense, and a production-ready SDK — without assembling it all from scratch.
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Why Building Sophisticated AI Agents Is Hard
A prototype works. A production agent that's secure, stateful, orchestrated, and auditable requires infrastructure most developers have to build themselves — or skip entirely.
Memory Doesn't Survive Between Calls
LLMs forget everything between sessions. Developers are forced to re-inject full context on every call, burning tokens and losing agent continuity across complex multi-step tasks.
Security Is an Afterthought
Prompt injection, adversarial inputs, and policy violations are typically discovered in production. Wiring in detection, enforcement, and audit trails as post-hoc additions creates fragile, inconsistent defenses.
Orchestration Logic Is Rebuilt Every Time
Multi-step workflows, conditional branches, and human approval gates require custom infrastructure for every project. There's no reusable, governed primitive — teams reinvent the same patterns repeatedly.
Code Drift Goes Undetected
Agents generate and modify code confidently but drift from their stated goals. Static linters catch syntax errors, not semantic misalignment — there's no feedback loop between what the agent intended and what it actually produced.
No Single SDK for the Full Stack
Developers stitch together multiple clients, auth libraries, and retry mechanisms to connect agents to their tools. Each integration adds its own auth, schema mapping, and error-handling overhead.
Agent Behavior Can't Be Verified
Without cryptographic proof of what an agent planned and executed, there's no auditable record for compliance, debugging, or incident response. Trust in autonomous agent outputs requires blind faith.
The Tools That Power AI Agent Development
Every layer of agent development — design, memory, orchestration, code analysis, security, and connectivity — covered by purpose-built tools that compose together.
Foundry
Design environment for tools and agents.
Foundry is where agents are built — Toolsmith forges validated query tools from natural language goals (SOQL, QBOQL, REST, OData), while the skill catalog stores every forged tool with full provenance. Define sub-tasks, browse the catalog, and save reusable skills that any agent can call by name.
View Foundry →Logic
Persistent symbolic memory across sessions.
Store facts, query knowledge, and define constraints that survive between calls — shared across all agents on your server. logic.remember and logic.query operate in sub-milliseconds with no LLM. Constraints defined once enforce business rules across all future workflows automatically.
View Logic →Flow
Multi-step orchestration with human approval gates.
Compose pipelines with conditional routing, parallel branches, and mid-workflow human approval gates. Plans are Prolog-validated before execution — no cycles, type-safe variable references, policy-compliant. Every verified plan receives a CTC and can be saved as a reusable skill.
View Flow →Invariant
Semantic code analysis to verify agent-generated code.
Extract structural and semantic facts from source code, query for patterns, and verify that diffs align with stated goals. invariant.diff_analyzer returns an alignment score, unexpected changes, and concerns — giving agents a neuro-symbolic feedback loop before code is committed.
View Invariant →Warden
Multi-tier prompt injection defense from day one.
Three independent detection tiers — canary probes, semantic intent analysis, and Prolog-backed policy evaluation — compose into a weighted ensemble. Use warden.ensemble as a gate step inside Flow to block adversarial inputs before they reach sensitive operations. Every evaluation is CTC-sealed.
View Warden →Conduit SDK
One import. Full agent infrastructure.
Available in Python, TypeScript, Rust, Elixir, and Ruby. Swap one import and get mTLS identity, semantic tool discovery, batch operations, and idiomatic wrappers for every DataGrout surface — client.logic, client.flow, client.warden, client.prism. Auth, retries, and cost tracking all handled.
View Conduit SDK →Discovery
Find the right tool by goal, not by name.
When your agent has access to hundreds of tools across integrations, Discovery lets it search by natural language goal instead of memorizing names. discovery.plan generates verified multi-step workflows with type bridging, cost estimates, and CTCs — saving reusable skills for future calls.
View Discovery →Inspect
Full execution history and CTC verification.
Inspect every agent action after it runs — inspect.execution-history, inspect.execution-details, and inspect.ctc-executions provide a tamper-evident audit trail. Debug decision-making, verify intent alignment, and produce compliance records without building a separate observability stack.
View Inspect →Scheduler
Time-based and event-driven agent execution.
Create recurring or event-driven tasks from natural language schedules. The scheduler auto-decomposes goals like 'alert me when pipeline failures spike' into sensor and conditional tasks — giving agents reliable, infrastructure-free execution triggers without writing any polling logic.
View Scheduler →Tasks
Background task management for long-running agent work.
When agent work outlives a single MCP response, Tasks keeps it alive. Agents can detach, poll status, and retrieve results across sessions — critical for development workflows that involve slow CI jobs, large code indexing runs, or multi-stage orchestrations that exceed response window limits.
View Tasks →Building a Legal Document Review Agent
An AI agent developer needs to automate complex legal document review — defining sub-tasks, integrating data sources, enforcing compliance policies, and debugging decision-making with full visibility.
Define the agent's sub-tasks
Foundry + ToolsmithThe developer uses Toolsmith to forge query tools for external legal databases — validated SOQL and REST specs generated from natural language goals. These tools are saved to the catalog with full provenance and become callable by name from any agent.
Build persistent knowledge
LogicAs the agent reviews documents, it uses logic.remember to store extracted facts, case precedents, and compliance rules. logic.constrain defines business rules — 'never summarize without source citation' — that are enforced across all future workflows automatically.
Orchestrate multi-step review
FlowFlow composes the full pipeline: extract → analyze → cross-reference precedents → flag anomalies → route to human review for high-risk clauses. flow.request-approval pauses execution for attorney sign-off on flagged items before proceeding.
Protect against adversarial inputs
WardenEvery document chunk passes through warden.ensemble before processing. The three-tier detection catches instruction-injection attempts embedded in document text — a common attack vector for legal document review agents exposed to adversarial inputs.
Verify agent-generated code
InvariantWhen the agent produces extraction scripts or analysis modules, invariant.diff_analyzer checks each change against the stated goal. If alignment score drops below threshold, the agent revises before committing — closing the feedback loop between intent and output.
Produce a tamper-evident audit trail
Inspect + CTCsEvery step is sealed with a Cognitive Trust Certificate. inspect.ctc-executions surfaces the full cryptographic proof of what ran, in what order, and with what policy compliance — shareable with clients or auditors via link, no account required.
What You Get Out of the Box
Everything a production-grade agent needs — without assembling it piece by piece.
Agents that remember
Logic provides sub-millisecond persistent symbolic memory shared across all agents on your server. No re-injection, no context bloat — facts persist and constraints enforce themselves.
Workflows that are verifiably safe
Flow validates every plan with Prolog before execution — catching cycles, type mismatches, and policy violations at compile time. Every executed plan is sealed with a cryptographic CTC.
Code quality built into the loop
Invariant closes the feedback loop between agent intent and agent output. Alignment scores and diff analysis happen before code lands — not after a production failure.
Security from the first call
Warden's three-tier ensemble detects prompt injection, semantic manipulation, and policy violations at the infrastructure level — not as an afterthought bolted on later.
One SDK for everything
Conduit gives you idiomatic wrappers for every DataGrout surface in Python, TypeScript, Rust, Elixir, and Ruby. mTLS, retries, and cost tracking are handled — you write agent logic, not plumbing.
Full audit trail without extra tooling
Inspect surfaces execution history and CTC-verified records for every agent action. Shareable cryptographic proof of what ran — for compliance, debugging, or client transparency.
Frequently Asked Questions
Common questions about building AI agents with DataGrout.
Ready to build your next AI agent?
Memory, orchestration, security, code analysis, and a production SDK — all in one platform. Start building in minutes.
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