How are AI agents different from RPA or chatbots?

AI agents interpret context, choose among actions and keep state across runs. Chatbots respond without acting, and RPA follows fixed rules that break when inputs change. On Major, the agent writes the repeatable part as code, so the routine path runs with RPA-like predictability and the model is reserved for exceptions that need judgment.

What makes an AI agent use case production-ready?

A use case is production-ready when it has clean source data, scoped credentials, a clear boundary around costly errors, and a durable place for state and audit logs. Major provides scoped credentials, role-based access, a managed database and audit logging by default. Cleaning the source data remains your job.

Why do so many agent projects fail?

Gartner predicts over 40 percent of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. A common cause is re-reasoning the same work on every run. Moving repeatable work into apps makes cost front-loaded and then flat as volume grows.

Which AI agent use case should I start with?

Start with work that runs often, has a clear owner and fails cheaply, such as ticket triage, invoice coding or lead routing. Frequency matters more than difficulty. Keep a human approval gate on anything that moves money or contacts customers until the audit trail has earned trust.

AI Agent Use Cases: A Predictable Pattern for Enterprise Work

Most AI agent use case lists are industry catalogs. Organize them around four repeatable patterns, reconcile, triage, route and draft-for-approval, and map each to a governed app.

Rahul RamakrishnanJuly 20, 2026Updated October 21, 2026

Team sorting sticky notes on a wall to map repeatable work patterns for AI agents

What are the main AI agent use cases?

The main AI agent use cases fall into four patterns: reconcile data across systems, triage incoming work, route items to the right owner, and draft outputs for a person to approve. The same four recur in finance, support, sales, HR and IT, and most production agents chain two or three of them into one workflow. If you pick pilots by pattern instead of by department, you can tell in advance where the human gate goes and which parts of the work will run the same way every time.

Major is the enterprise platform where agents build the software they run on. Each pattern below maps to a deterministic app the agent builds once and then runs, with its own database, scoped credentials and audit log. The agent keeps its reasoning for the judgment calls.

Why pick use cases by pattern, not by industry

Industry catalogs tell you that "finance uses agents." They do not tell you what to build on Monday. Patterns do, because each one has a known shape: the systems it touches, the step that needs judgment, and the point where being wrong gets expensive.

Gartner predicts over 40 percent of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Those three causes line up with one design habit: letting a model re-reason the same task on every run. Cost climbs with usage, behavior varies, and nobody can reconstruct what happened. Patterns push you toward the opposite habit, where the repeatable path becomes code.

The four patterns

Reconcile: pull facts from several systems into one correct record. Typical connectors: HubSpot, Salesforce, Stripe, a Postgres database. Human gate: approve merges or write-backs above a threshold.
Triage: classify and score incoming work. Typical connectors: Zendesk, Gmail, Slack. Human gate: confirm the highest-severity calls.
Route: send each item to the owner or downstream system. Typical connectors: Salesforce, Slack, GitHub. Human gate: sign off on escalations.
Draft for approval: assemble context and write an output that stops before it is sent. Typical connectors: Gmail, Notion, your CRM. Human gate: approve before send.

Reconcile

A payment lands in billing, the contract terms sit in the CRM, and the ledger lives somewhere else. The agent matches all three, writes back the corrected record, and flags any case where the numbers disagree beyond a set amount. The gate matters because a bad merge does not announce itself. It quietly carries a wrong number into every report downstream.

Triage

A support queue, an alert stream or an inbox of inbound leads arrives as unstructured text that needs a category and a severity before anyone can act. The agent reads each item, assigns both, and hands over a ranked list. Keep a person confirming the top-severity calls, since mislabeling a critical case as routine is the failure triage exists to prevent.

Route

Once work is classified, something has to open the right ticket, notify the right owner or hand the item to the next system. A small classification error here becomes a case sitting in the wrong queue for a week. Escalations are the natural approval point.

Draft for approval

A collections note, a customer reply, a summary for a system of record. The agent does the tedious assembly and writing, and a person owns what actually goes out. It is the safest first pilot because the gate is part of the pattern.

A finance workflow shows how they chain. The agent reconciles a payment, triages whether it is disputed, routes disputes to an owner, and drafts a collections note for the rest.

Which use case to start with

Start with work that runs often, has a clear owner and fails cheaply: invoice coding, ticket triage, lead routing. Frequency matters more than difficulty. A workflow that runs daily pays back the setup and gives you enough examples to tune the rules. Avoid anything that moves money or contacts customers without a review step until the audit trail has earned trust.

A workflow that happens once, such as a one-off analysis, does not need an app. A chat session is the right tool there. Major earns its place when the work repeats, crosses several systems, and someone will eventually need to explain to an auditor what it did last quarter.

Build any of the four patterns on Major

Here is the concrete shape, using ticket triage and routing as the example.

Connectors: Zendesk for tickets, Slack for escalations, Salesforce for account context. Credentials are scoped per connector and applied where the agent acts.
The app that holds state: a triage app with managed tables for tickets, classifications, owners and decisions, plus a review view where a support lead can see and override every call.
The agent and its judgment: the first time, the agent works out the classification rules and builds the app. On later runs it only reasons about the ambiguous tickets, and the app handles the rest in deterministic code.
The trigger: a workflow fires on new Zendesk tickets, or on a schedule for batch work such as a nightly reconciliation.
The gate: high-severity tickets post to a Slack channel for human confirmation before routing. The approval runs under the same SSO and permissions as the rest of your software and is logged against the approver.
Governance: role-based access decides who can view or change the app, and the audit log records each action the agent took, under which credentials, and what changed.

Because the repeatable path is code, token cost is front-loaded when the agent builds the app and then flat as volume grows. The same app is also reusable: another team can run it, and IT can oversee it in one place.

Start building on Major with a pattern from this list, and describe the workflow in a sentence.

Where agents still need people

Agents handle repetition and drafting. People own exceptions, approvals and the calls where being wrong is expensive. That is why each pattern ends at a gate. The question to ask of any deployment is whether you can audit every action after the fact and reconstruct why the agent did what it did.

This scoping matters too: the agent still needs reasonably clean source data to reconcile against, and a person still has to decide where the gates belong. Major removes the part that never needed a model, which is running the same reliable path the same way every time. It does not remove the model, and it does not remove the people running the workflow.

Start building on Major

Pick one repeatable workflow from the four patterns, connect the systems it touches, and let an agent build the governed app that runs it. Reason once, run forever.

Frequently asked questions

What are the main AI agent use cases?: The main AI agent use cases cluster into four patterns: reconciling data across systems, triaging incoming work, routing items to the right owner, and drafting outputs for human approval. The same four appear in support, finance, HR, sales and IT, usually chained into one workflow. On Major, the agent builds each repeatable step as a deterministic app it then runs.
How are AI agents different from RPA or chatbots?: AI agents interpret context, choose among actions and keep state across runs. Chatbots respond without acting, and RPA follows fixed rules that break when inputs change. On Major, the agent writes the repeatable part as code, so the routine path runs with RPA-like predictability and the model is reserved for exceptions that need judgment.
What makes an AI agent use case production-ready?: A use case is production-ready when it has clean source data, scoped credentials, a clear boundary around costly errors, and a durable place for state and audit logs. Major provides scoped credentials, role-based access, a managed database and audit logging by default. Cleaning the source data remains your job.
Why do so many agent projects fail?: Gartner predicts over 40 percent of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. A common cause is re-reasoning the same work on every run. Moving repeatable work into apps makes cost front-loaded and then flat as volume grows.
Which AI agent use case should I start with?: Start with work that runs often, has a clear owner and fails cheaply, such as ticket triage, invoice coding or lead routing. Frequency matters more than difficulty. Keep a human approval gate on anything that moves money or contacts customers until the audit trail has earned trust.