One agent per job, not one agent for the company
A general purpose agent given broad access to a business has no defined boundary. Nobody can state what it should do, test whether it did, or constrain what it reaches. It performs impressively in a demonstration, then becomes untrustworthy the moment real consequences attach to its output, because nobody can specify what correct behavior looks like across an entire company.
The deployments producing measurable results are built the opposite way. A set of narrowly scoped agents, each assigned to one function, each wired into a workflow that already exists, and each with an input, an output and a boundary someone wrote down. Individually they are modest, but collectively they absorb a meaningful share of the repetitive interpretive work that consumes a team’s week.
What a well-scoped agent looks like
Scope is the decision that determines whether one of these is useful, and the test is whether you can write down what the agent does in one sentence and then verify it did that. Some agents we build, described generically:
Inbound document interpretation
A purchase order, supplier invoice or emailed request arrives in whatever format the sender chose. The agent reads it, extracts the structured values, matches them against existing customers and items, and drafts the entry for review. It does not commit the record. It removes the typing and the lookup.
Exception surfacing
Two systems disagree about a quantity, a price or a customer record. The agent examines the disagreement, proposes a resolution with its reasoning attached, and places it in a review queue ordered by consequence.
Draft response preparation
An inbound customer message arrives. The agent identifies what is being asked, retrieves the relevant order or account context, and prepares a draft reply for a person to send, edit or discard. The person still owns the relationship, while the blank page is gone.
Monitoring with judgment
Rather than a threshold alert firing whenever a number crosses a line, an agent evaluates whether a pattern actually warrants attention given recent context, which reduces the alert fatigue that causes teams to ignore monitoring altogether.
Classification and routing
Incoming work is categorized and directed to the right person or queue based on what it actually concerns rather than on a keyword rule that misfires on unusual phrasing.
Each of those is small. None of them requires the agent to understand the whole business.
The approval path is the design
The most consequential choice in building an agent is not what it does. It is what it is permitted to finish. We divide this along the line of consequence. Where a wrong answer costs money, alters inventory, or creates a commitment to a customer, the agent proposes and a person approves before anything commits. Where the cost of being wrong is low and easily reversed, the agent proceeds and a person reviews afterward on a sample basis.
Beneath the approval path sits a second layer of protection that does not depend on anyone paying attention. Agents write into systems that enforce their own rules, meaning schema validation, referential integrity and required fields, so a malformed proposal is rejected mechanically. An agent should not be the only thing standing between a bad output and your records. Every proposal, approval and override is recorded, so the agent’s authority can be widened or narrowed on evidence rather than impression.
Agents fail differently than software
"Traditional software fails loudly. An agent fails quietly and fluently, producing an answer that reads exactly like a correct answer and is wrong."
That difference should shape how these systems are operated. It means the review queue matters more than the dashboard, because a queue forces someone to look at individual outputs rather than at an aggregate that hides errors. It means starting an agent in proposal only mode regardless of how confident the design is, and widening its authority once there is a record showing it earns the trust. And it means a person needs to remain competent at the task the agent performs, because a team that has lost the ability to do the work itself cannot evaluate whether the agent is still doing it correctly.
Where agents genuinely do not belong
Some processes should stay with people, and pretending otherwise helps nobody. Anything where the relationship is the product, meaning the conversations where a customer needs to know a human is engaged. Anything with a legal or regulatory obligation attached to the judgment. Work rare enough that the agent will never see enough cases to be worth building or verifying. And anything where the process itself is broken, because automating a bad process produces the same bad outcome faster and at greater volume. Frequently the correct first step is fixing how the work flows, not adding an agent to it.
How the engagement runs
Discovery, design and the substantial majority of the build happen through screen shared working sessions. Onsite is available when the work genuinely earns it. The surrounding architecture that makes agent output safe to act on is covered in custom AI integrations. Deciding which processes to start with is covered in AI consulting.