It comes back.
The job repeats often enough that the time spent checking, copying, chasing, or preparing adds up.
AI agent use cases
Where agents get put to work.
AI agents can qualify leads, resolve support tickets, publish content, prepare reports, and coordinate operations. Explore five common use cases and see the work behind them.
Meet some all-star agents
A job, carried through.
An AI agent uses connected tools to read information, make decisions within your rules, and take action. Machine-like builds, runs, and maintains the agents. Your team sets the direction and keeps the decisions that need a person.
Sales & lead management
Respond while the lead
still wants to talk.
You paid to bring the right people to your business. Don’t make them wait while someone checks a record, finds the right owner, and remembers to follow up.
Sales agents read each inquiry, check it against your criteria, and keep the next step moving across your CRM, inbox, and calendar.
- Qualify and route. Assess fit, assign an owner, and attach the reasoning.
- Reply and book. Send an approved response and coordinate available times.
- Follow through. Log the outcome and stop follow-ups on a reply, booking, or opt-out.
What comes back
A lead with context.
An owner.
A next step.
The CRM reflects what happened, who is responsible, and what happens next.
You decide
Who qualifies, how often to follow up, and which conversations go straight to a person.
Customer support
Clear the routine queue.
Give people the hard cases.
Much of support happens before the answer: find the account, check the plan, search the docs, update the ticket. Support agents handle that sequence and respond within your policies.
Routine requests get handled. Exceptions reach the right teammate with the customer’s context, the work already done, and the decision still needed.
- Triage the request. Identify the issue, urgency, and customer context.
- Resolve within policy. Send approved answers and update the record.
- Prepare the escalation. Give a person enough context to act.
What comes back
A handled request.
A current record.
A resolved ticket or a prepared escalation, with the actions and sources recorded.
You decide
Response policies, refund limits, sensitive cases, and the point at which a person takes over.
82%
of Tier 1 support tickets automated at fitDEGREE, across triage, context lookup, approved responses, and ticket updates.
Source: fitDEGREE customer story
Marketing, content & SEO
Keep good content moving
from idea to published.
Knowing what to publish is only the start. Research, writing, approvals, metadata, publishing, and checking the page still have to happen.
Marketing agents carry that work across your content and search tools. They research buyer questions, draft in your voice, prepare structured data, publish approved work, and monitor search visibility.
- Find the opportunity. Turn search data and buyer questions into useful briefs.
- Produce and publish. Move content through your editorial and approval process.
- Check and improve. Verify the live page and identify what needs attention next.
The business result
Our search work with Lake helped it reach 47% AI citation share on tracked unbranded lake-rental prompts in 21 days. See the engagement ↗
Meet Fin, our content agent
The job ends
on a live page.
Fin researches, writes, illustrates, and publishes our Field Notes, then checks the result. Here is an excerpt from one recorded run.
- Assignment dispatched
- T+00:00
- First article live
- T+07:25
- Saved content checked
- Match
- Article and index checked
- Verified
You decide
The positioning, claims, editorial standards, and what requires approval before publication.
Structured data is part of the job, too. Meet Gizmo, a customer’s schema agent ↗
Reporting & analysis
Get to the decision
without rebuilding the report.
Monday’s numbers shouldn’t consume Sunday night. Reporting agents pull the data, check freshness and definitions, flag inconsistencies, and assemble the report your team needs.
Sales rollups, client recaps, campaign readouts, and operating reports arrive with their sources and unresolved questions attached.
- Gather the evidence. Pull from approved sources on the agreed schedule.
- Check the numbers. Flag stale data, missing records, and conflicting definitions.
- Prepare the readout. Assemble the report, changes, and questions for review.
What comes back
A report ready
for your judgment.
The numbers, where they came from, what changed, and what needs a closer look.
You decide
What the findings mean, which recommendations to make, and what reaches clients or leadership.
Inside a customer’s analytics team
For a performance marketing company, we deployed agents for recurring analysis prep, data checks, campaign monitoring, and reporting. Decision Scientists retained experiment design, interpretation, and final recommendations.
Operations & coordination
Keep the handoffs
from becoming your job.
A signed client needs documents. A stock shortage needs a purchasing decision. Work stalls when the information is scattered and the next step depends on someone remembering.
Operations agents collect missing information, reconcile records, prepare actions, and follow through across your systems. Each job has a clear finish and a person responsible for exceptions.
- Collect what is missing. Request documents, confirmations, or updated records.
- Prepare the next action. Create kickoff tasks, replenishment recommendations, or purchase-order drafts.
- Close the loop. Route approvals, update records, and flag anything still unresolved.
What comes back
The next step,
ready to move.
- For client onboarding
- A complete intake, kickoff tasks, and a clear list of anything still missing.
- For inventory
- Replenishment recommendations, orders ready for approval, and supplier follow-up.
You decide
Purchasing limits, contract exceptions, access permissions, and other commitments.
Start with the job
that keeps stealing time.
You don’t need an AI strategy for the entire company. You need one useful job to hand off and a clear way to tell whether it worked.
Explore more jobs by role and industryYou can show “done.”
A good report, a correctly routed lead, or a completed intake gives the agent a concrete standard.
The facts are reachable.
The information lives in systems or documents the agent can access with your permission.
You can draw the line.
You know which actions can proceed and which decisions need a person.
The work is yours.
Running the agent is ours.
Machine-like builds custom agents around your tools, rules, and definition of done. We handle deployment, monitoring, maintenance, and fixes as the job evolves.
Show us the job.
Bring the recurring task and an example of good work. Together, we define the output, connections, and approval points.
We put the agent to work.
You authorize the connections. We configure and test the agent against the agreed job before it goes live.
Judge it on real work.
Your free 14-day trial starts when the agent goes live. Review what it completes, then decide whether to keep it.
No setup or integration fees. No credit card required for the trial.
See pricing and what’s includedBefore you hand
off the job.
A few practical questions about using AI agents in your business.
Talk through your use caseWhat are common AI agent use cases?
Common business uses include lead qualification and follow-up, support ticket handling, content and SEO, reporting and analysis, and operations such as onboarding or inventory coordination. Each use case becomes a specific job when you define the inputs, finished output, and rules.
How is an AI agent different from a chatbot?
A chatbot is a conversational interface. An AI agent can use connected tools to carry out work, such as updating a CRM, preparing a report, or publishing an approved page. Some chatbots include agent capabilities. Look at the actions the system can take and how it checks the result.
Can we use agents without a technical team?
Yes. Machine-like builds and operates the agent for you. Your team provides business context, authorizes the relevant systems, and handles the approvals and exceptions agreed during scoping. We own the build, monitoring, and maintenance.
How do we control what the agent does?
We define permissions, approval points, and escalation rules before launch. You can require sign-off before messages go out, content is published, or a purchase is made. The agent’s authority is specific to its job, and exceptions go to the designated person.
How do we know the output is good?
Start with examples and acceptance criteria your team agrees on. Checks confirm the relevant records, required fields, and completed actions; run records show what happened. Editorial quality, interpretation, and other judgment calls stay in the review process. Your trial tests the work against those standards.
Should we hire a person instead?
Hire for the relationships, leadership, and judgment a role needs. Agents can take on the recurring preparation and follow-through around that role. If a task only moves a clean field between two systems, a simple automation may be enough. Choose based on the work, not the label.
What does a managed AI agent cost?
Machine-like charges $297 per workload per month for one to three workloads, or $197 per workload per month for four to five. Six or more receive custom pricing. A workload is a scoped, recurring job. Setup and integration fees are $0, and the 14-day free trial begins at go-live. See current pricing and scope.
Get back tothe work of humans1
Your work should be about creativity, solving problems, and building relationships.
Leave the machine-like tasks to us.