No extra effort
Experts just do their jobs. No re-recording, no process-mapping workshop, no interviews.
How it works
How ModelNex Works
It learns your expert, then works with them across your APIs, MCP tools, and screens. Your team owns what it learned.
Step 1 · Observe
Watching real casework, not a recording session.
Most workflows are never written down; the judgment behind them lives in a few experts’ heads. Through the extension, agents observe experts as they work live cases. A demonstration or an uploaded JSON definition can also seed the workflow directly.
Scoped and visible
Observation is scoped to the applications you nominate, and every expert sees a recording indicator.
Real work, not the doc
It watches how your best people actually work, not how the process doc says they do.
Step 2 · Capture
The why, not the what.
Actions alone produce a rule-follower that breaks on the first unusual case. What transfers is the judgment behind them, and capturing it is the part we have spent the most time getting right. How the agent earns that judgment is what we show in the demo.
No interruption here
Nothing is asked of the expert at this stage. No annotation, no interruption while they work their own cases.
Confirmed at approval
Experts confirm or correct what the agent learned later, at the point of approval.
Role, not name
What it learns is attributed to a role, not a name, and is never used to evaluate individuals.
Step 3 · Execute
The agent works the case where the work lives.
Most steps are API and MCP calls straight into your systems. When a system only exposes a screen, the agent drives the browser instead. Either way it reasons from the captured rationale rather than a fixed procedure.
API and MCP first
Steps call your systems directly, with no browser in the loop. Browser steps drive the page only when a system has no API worth using.
Verified before advancing
Before/after snapshots verify the exit criteria before the run advances.
Reflect and retry
On failure, the agent reflects and retries.
Step 4 · Approve
Guardrails for human approval.
You set the guardrails: which calls require a human, and where a run must stop. This is also where the agent learns, because the expert is confirming or correcting the rationale it acted on, not just the click.
Guardrails are yours to set
Route any step to a specific person for review, approval, or correction. Novel and low-confidence cases stop for a human by design.
Corrections capture judgment
The agent shows the rationale behind what it is about to do. Confirming takes a click; correcting takes a sentence, and that correction captures the expert’s judgment.
Full event trace
The run resumes where it left off, and every action lands in a structured event trace for audit.
Step 5 · Learn
Expert judgment, verified.
Verification runs both ways. The agent asks an expert to validate the judgment it applied, and it learns just as much when an expert corrects it unprompted.
Validation on request
The agent asks for validation on the calls it is least confident about.
Corrections carry reasoning
Corrections made at approval are captured with the reasoning behind them.
Applied to the next case
Verified judgment updates what the agent applies on the case after this one.
How we compare
Where ModelNex sits against the alternatives.
Evaluators usually weigh ModelNex against three other categories. The difference in every case is whether the judgment is captured or re-specified.
Horizontal agent platforms
Agentforce, Bedrock AgentCore, and Azure AI Foundry are toolkits: models, tools, and orchestration, with everything vertical built from scratch on top. They are what an internal team builds with, not a product that competes with one. Their tools plug into any ModelNex step.
Agent-builder frameworks
LangChain and the OpenAI Agents SDK give you the reasoning loop, not the judgment. They have no opinion on how your best reviewer works a case and no way to learn it. That part is the product.
Browser and back-office automation
With Skyvern, Asteroid, Sola, and Poetic the agent is in the navigation, not the decision: you still specify the procedure. That works for deterministic tasks and breaks on anything needing interpretation. ModelNex learns the judgment behind the procedure: why your expert decided, not where they clicked.
Healthcare AI vendors
Anterior and Cohere Health sell you their decision: their clinical policy, their interface, their model producing determinations your medical directors are asked to sign. ModelNex does the groundwork instead. The agent works the case the way your reviewers do and lays out the evidence, and your reviewer decides on it.