What kind of work is a good fit for an AI agent?

Work that is repetitive, arrives on a schedule or from a queue, and has a definition of done that can be checked. Triage, reporting, research, content pipelines and data clean-up are typical. Work that needs judgment on every item, or where a wrong answer is expensive and hard to reverse, should stay with a person or run behind an approval queue.

How is this different from a script or a Zapier workflow?

A script does exactly the same steps every time and breaks when the input changes shape. An agent reads the situation, decides which steps apply, and handles variation — which is exactly why it needs verification gates and budgets that a script never did. If your work is genuinely identical every time, a script is cheaper and I will tell you to use one.

What does it cost to run once it is built?

It depends on the model and the volume, and the honest answer is that you will know precisely, because per-run spend is measured and capped. Most business automations cost far less per month to run than the hours they replace. I put the budget in writing before the system goes live.

Do I need a technical team to keep it running?

No. The system is delivered with a runbook, monitoring that tells you when something needs attention, and configuration that a non-developer can change for schedules and budgets. For anything deeper, any competent developer can pick up the code — it is deliberately unexotic.

That is the only way I recommend starting. One task with a clear check, run on a schedule, watched for a couple of weeks. When that is boringly reliable, we add the next one.

Your team spends hours a week on work a system should be doing.

Agentic automation

Agents that run on a schedule and finish real work end to end — triage, reporting, research, content pipelines — instead of drafting something a human then redoes.

The situation

Most businesses that ask about AI automation already know which work is the problem. It is the weekly report that takes a morning to assemble. The inbox that has to be triaged before anyone can start. The research that gets done badly because nobody has time to do it well. You are not looking for a chatbot. You are looking for the work to be done.

Agentic automation is the difference between an assistant that drafts something a person then redoes, and a system that picks up a queue of tasks, executes them end to end, checks its own results, and only involves a human when a check fails. I build the second kind, and I run one on my own work every night.

How I approach it

Start from the queue, not the model

The first thing I want to see is the list of work: what arrives, how often, what 'done' looks like, and who notices when it is wrong. An agent is only as useful as the definition of done it runs against, so we write that down before a single prompt exists. If the work cannot be described as a task with a verifiable result, it is not ready for an agent yet — and I will say so rather than sell you one.

Verification gates over model confidence

A language model will tell you it finished. That is not evidence. Every task in the systems I build carries a check — a command, a query, a diff, a test — that must pass before the task is marked complete. If the check fails, the agent retries with the failure in front of it. If it fails three times, it stops and escalates to a person. The model's opinion of its own work never decides the outcome.

Budgets that actually stop things

Unattended systems spend money while you sleep. Each run gets a hard budget measured per task and per run, enforced by the orchestrator rather than requested of the model. When the budget is reached the run ends, cleanly, with a log of what it got through. You will know the cost of a night's work before it happens, not on the invoice afterwards.

Escalation and heartbeats, so silence means something

An automation that fails quietly is worse than one that never existed, because you stop checking. Everything I ship reports a heartbeat, and a missed heartbeat is itself an alert. Failures escalate to a channel a human reads. The goal is that when nothing arrives from the system, you can trust that nothing needed to.

Hand over a system, not a dependency

You get the orchestration code, the task definitions, the verification commands, the budget configuration and a plain-English runbook. Any developer can read it. You are not renting a black box from me, and you should not have to call me to change a schedule.

What gets delivered

Task orchestration with priority scheduling
Verification gates: a task is done when a check passes, not when the model says so
Enforced spend budgets, measured per run
Failure escalation and heartbeat monitoring

What it costs to get wrong

An agent that marks work done because the model said so, so nobody re-checks it until a customer does.
An unattended loop that retries a failing task hundreds of times overnight and hands you the API bill.
A pipeline that silently stopped weeks ago while the team assumed it was running — I have had exactly this happen to me, which is why every system I build now heartbeats.
A prototype that impressed in the demo and cannot be trusted with a Tuesday morning, so it quietly becomes a tool one person uses sometimes.

Why you can believe this

I built and run an orchestration control plane that executes queued work overnight under a hard budget, verifies every task against a command's exit code, and blocks after three failures rather than looping.

Explore the work Read the ai-os case study Try the AI Operations Lab

Questions buyers ask

What kind of work is a good fit for an AI agent?: Work that is repetitive, arrives on a schedule or from a queue, and has a definition of done that can be checked. Triage, reporting, research, content pipelines and data clean-up are typical. Work that needs judgment on every item, or where a wrong answer is expensive and hard to reverse, should stay with a person or run behind an approval queue.
How is this different from a script or a Zapier workflow?: A script does exactly the same steps every time and breaks when the input changes shape. An agent reads the situation, decides which steps apply, and handles variation — which is exactly why it needs verification gates and budgets that a script never did. If your work is genuinely identical every time, a script is cheaper and I will tell you to use one.
What does it cost to run once it is built?: It depends on the model and the volume, and the honest answer is that you will know precisely, because per-run spend is measured and capped. Most business automations cost far less per month to run than the hours they replace. I put the budget in writing before the system goes live.
Do I need a technical team to keep it running?: No. The system is delivered with a runbook, monitoring that tells you when something needs attention, and configuration that a non-developer can change for schedules and budgets. For anything deeper, any competent developer can pick up the code — it is deliberately unexotic.
Can we start small?: That is the only way I recommend starting. One task with a clear check, run on a schedule, watched for a couple of weeks. When that is boringly reliable, we add the next one.