Custom AI workflow systems for the way your business already works.
DAG Studio + DAG Workbench + Coding Workspace + Custom Skill Hub
Jintellar Solutions combines DAG Studio authoring, DAG Workbench run review, Coding Workspace execution, and custom Skill Hub tooling so teams can start from governed starter packs, then tailor workflows around their own systems, documents, approvals, and operating rules.
Author
DAG Studio
Author a reusable agent workflow from a plain-language business requirement, then review the nodes, tools, approvals, and outputs before it runs.
Run
DAG Workbench
Run published workflows with graph-level visibility into node progress, approvals, outputs, evidence, and execution history.
Execute
Coding Workspace
Route the technical nodes of a workflow into a governed workspace for code execution, file work, tests, notebooks, and data processing.
Extend
Custom Skill Hub
Start from starter packs, then add company-specific tools, internal APIs, and workflow packs that fit your operating environment.
What The Workflow Is
A custom agent workflow is a reusable operating profile for one business job.
It defines what comes in, what the agents do, what tools they can touch, what controls apply, and what has to come out before the run is considered complete.
Inputs
Documents, tickets, filings, repository context, datasets, or inbound requests.
Agent steps
Reasoning, retrieval, drafting, tool actions, routing, checking, and decision steps.
Controls
Approvals, capability boundaries, schedules, stop conditions, and audit requirements.
Outputs
Reports, updates, case files, patches, approvals, evidence bundles, and final decisions.
DAG Studio
Author the workflow profile before anything runs.
DAG Studio is the workflow authoring surface. A user describes the business job, watches the graph build live, reviews the node contracts, asks for revisions, and only then saves or publishes the reusable profile.
Authoring sequence
Describe, build, review, publish
Describe the job
Start with a real business workflow such as support triage, repo patch review, legal research, filing preparation, or internal operations.
Author the profile
DAG Studio builds the graph live, drafts node contracts, selects tools, defines schemas, and proposes the approval and evidence plan.
Review and adjust
Your team can lock nodes, edit prompts, adjust tool scope, add review gates, run a mock pass, and refine the profile before publication.
Publish into execution
Published profiles run through Workbench and the governed runtime, where every step, artifact, approval, and side effect stays observable.
AI helps author the workflow, but the draft stays reviewable and editable before it becomes a published profile.
Live graph build
Watch nodes, edges, prompts, schemas, tools, and guardrails appear as the draft is authored.
Locked revisions
Protect any node, prompt field, or output rule that the team does not want AI to rewrite.
Mock validation
Check the structure, contracts, and publish readiness before the workflow is promoted into execution.
DAG Workbench
Run the published workflow where teams can observe the work.
DAG Workbench is the runtime surface for governed execution. It shows the graph, node progress, evidence, outputs, and review state for published workflows after they leave DAG Studio.
- Node-by-node workflow progress and branch transitions
- Approvals, evidence, outputs, and exception handling
- Document, research, filing, and operational workflow runs
- Replayable execution history for governed run review
DAG Workbench is the published workflow runtime surface. Technical nodes that need code, files, tests, or notebook execution can be routed separately into Coding Workspace.
DAG execution graph and run review
Use DAG Workbench to monitor graph execution, inspect node evidence, review outputs, and understand exactly what happened during the run.
Coding Workspace
The controlled execution layer for technical workflow steps.
Coding Workspace is JintellarCore's governed execution layer for workflow nodes that require code, files, notebooks, tests, parsers, or data processing.
Typical routing pattern
Most workflow steps stay in governed research, filing, review, or reporting nodes. Only the technical node is routed into Coding Workspace, then the output returns to the workflow for the next governed step.
Technical node execution
When a workflow step requires code execution, file generation, notebook analysis, testing, or data transformation, JintellarCore routes that node into the governed Coding Workspace.
Validated workspace boundaries
Execution stays inside validated workspace roots with approved tools, constrained actions, audit logs, artifacts, and replayable evidence.
Technical work products
Use it for scripts, tests, parsers, spreadsheets, notebooks, file edits, generated artifacts, and other technical side effects a workflow step may need.
Separate from DAG Workbench
DAG Workbench shows the workflow run. Coding Workspace is the governed execution layer for the technical parts of that run.
Detect
Workflow node
Research, filing, review, or reporting step identifies a technical action.
Execute
Coding Workspace
Execute code, file, notebook, test, or transform work inside governed boundaries.
Continue
Return to workflow
The artifact, output, or result moves back into the DAG for review, approval, or delivery.
Skill Hub packs for company-specific work
Move from generic building blocks to tailored capabilities by packaging the exact tools and actions your company needs.
Custom Skill Hub Tooling
Start from starter packs, then build the company-specific packs that make the workflow real.
Jintellar can provide the basic packs for common workflow patterns, then work with your team to create custom packs and governed tool modules around your systems, data, APIs, and review rules.
Starter packs
Custom packs we build with you
Delivery Model
How we turn the workflow into a working system.
The solution starts with governed building blocks, then becomes company-specific through profile design, runtime review, and custom tooling creation.
Step 1
Start with starter packs
Use the baseline building blocks for intake, retrieval, review, approval, reporting, repo work, and governed execution.
Step 2
Tailor the workflow
Model the company process in DAG Studio so the workflow matches your documents, systems, controls, and output expectations.
Step 3
Build custom Skill Hub tooling
Add the custom packs, internal adapters, and skill modules needed to operate inside your real environment rather than a generic demo stack.
Start with a governed starter pack, then tailor the workflow and tooling to your company.
DAG Studio authors the profile, DAG Workbench runs and reviews it, Coding Workspace executes technical nodes, and custom Skill Hub packs connect it to the systems and actions your team actually uses.