An AI agent is software that uses an AI model to carry out a multi-step task on your behalf, such as reading inputs, looking things up in your systems, drafting an output and handing it to a person for review, and a custom agent is one built around your firm’s specific workflow, data and rules.
The useful question for most firms is not “should we have agents?” but “which of our recurring workflows would be better run this way, and who should build and own it?”
Who this is for
COOs, operations leads and technology leads who keep hearing about agents and want a grounded way to decide whether to build one, and with whom.
The short version
- An agent is a chat assistant plus access to tools and a defined job. The value comes from the workflow design, not the model alone.
- Good first agents handle frequent, well-understood, checkable work, with a person reviewing the output.
- Choose builders who ask about ownership, permissions and monitoring, not just features.
Agents in plain English
A chat assistant answers when you ask. An agent is given a job and the means to do it.
A simple way to picture one:
- Trigger: something starts it, such as a new email, a file landing in a folder, or a weekly schedule.
- Inputs: it gathers what it needs, perhaps the document, related records in your CRM, or last month’s version.
- Steps: it works through a defined sequence: extract, compare, summarize, draft, flag issues.
- Output: it produces something concrete, like a draft memo, an updated record or a checklist of exceptions.
- Handoff: a person reviews, edits and approves before anything leaves the firm or changes an important record.
The more of those steps that are clearly defined, the more reliable the agent tends to be. The vaguer the job, the more it behaves like an unpredictable chat session.
What makes a good first agent
Look for workflows that score well on all of these:
- Recurring. It happens weekly or more, so the build effort pays back.
- Well understood. Someone can describe the steps and what a good result looks like.
- Multi-system. It currently involves copying between email, documents, spreadsheets or a CRM.
- Checkable. A reviewer can quickly confirm the output is right.
- Recoverable. If the agent gets something wrong, it is caught before it causes harm.
Typical shapes include structured summaries of incoming documents, first drafts of recurring reports, triaging and routing inbound requests, keeping records up to date from meeting notes, and flagging exceptions in data that would otherwise be checked by hand.
Weaker first candidates are high-judgment decisions, anything that sends external communications without review, and workflows nobody can describe consistently.
Before you build: a readiness checklist
- The workflow has a named owner who wants it improved.
- The people involved already use AI tools and understand their strengths and limits.
- You know which systems the agent needs to read from and write to.
- You have decided what data it may access and which actions need human approval.
- You have a few real examples of inputs and good outputs to test against.
- Someone will monitor it and fix it when things change.
If you can’t tick most of these, training or a strategy step is probably the better first move.
What good agent engineering includes
Whoever builds your agent, these should be part of the work, not optional extras:
- Least-privilege access. The agent sees and does only what its job requires.
- Human approval for consequential actions. Sending, deleting, paying or changing key records should wait for a person.
- Testing on real examples. A set of realistic cases, checked before launch and after changes.
- Logging. A record of what the agent did and why, so problems can be traced.
- Handling untrusted input. Emails and documents can contain text designed to mislead an AI system. The design should assume that.
- Documentation and handover. Your team should understand how it works and how to change it.
Who can build it
Broadly, firms have four options: an internal engineer, a freelance developer, a specialist AI firm, or a vendor whose product includes configurable agents. Each can work. The deciding factors are usually who will maintain it afterwards, how sensitive the data is, and whether the builder understands the workflow as well as the technology.
Questions worth asking any builder:
- How will you learn our workflow before building?
- What access will the agent need, and how will you limit it?
- How will we test whether it works, and keep testing after launch?
- What happens when the underlying model or our systems change?
- Who can maintain this if you’re not available?
Common mistakes
- Starting with the most impressive idea. Reliable, boring workflows make better first agents.
- Giving broad access for convenience. Narrow permissions are safer and easier to reason about.
- Removing the human too early. Earn autonomy with a track record.
- No owner after launch. Unmaintained agents tend to drift and break quietly.
- Building before people understand AI. Users who don’t trust the output won’t use the agent.
When you do not need us
If a vendor tool you already use offers agent features that fit the workflow, try those first. And if you have an internal engineer with time to own the build and the readiness checklist is mostly ticked, you may only need a short review, or nothing.
How Ruby helps
- AI engineering: secure, customized agents and integrations, built with review, logging and handover.
- AI strategy: choosing the right first workflow and deciding whether to build at all.
- AI training: so the people working alongside the agent trust it appropriately and can improve it.
Start with one practical hour of AI training, or bring us a specific workflow to discuss.