The First Jobs to Give AI Agents Before Models Get Smarter

When a new model ships, the companies that see the gains first already have agents doing real work. They have already decided which systems an agent may read and who signs off on its output. This post covers the jobs we recommend handing to AI agents first and how to choose among them. It also explains why those first builds matter more for what they set up than for the hours they save in the first month.

Four tests for a good first job

We use four tests when a client asks where to start. The first is volume. A task that happens a few times a month will not repay the setup, while one that lands many times a day gives the agent enough practice for its errors to show up quickly and gives you savings large enough to notice.

The second test is whether the work is document-heavy. Agents are strongest when they read a stack of documents and draft something from what they find. Work that lives in one person's head makes a poor first choice, because the agent has nothing to read.

The third is a clear right answer. A certificate either matches the policy or it does not. An invoice either agrees with the purchase order and the receipt or it does not. When a reviewer can tell quickly whether a draft is right, you can measure accuracy properly and keep the review short.

The fourth test is a person who can approve. Every first build needs a named reviewer who already does this work and has the authority to sign off the output. If nobody owns the approval today, the agent will expose that gap within days. Before any build starts, we also time the current process on a sample of real cases, because that baseline decides later whether the agent was worth it.

Insurance: certificates and renewal comparisons

For an independent insurance agency, certificates of insurance pass all four tests. Requests arrive by email all day from contractors and property managers. The agent reads the request and finds the client's current policies. It checks the requested limits and additional insured wording against what the policies and endorsements actually say, then drafts the certificate for a person to release.

Anything the agent cannot trace to an endorsement goes to a licensed person instead of onto the certificate. That rule protects the agency's errors and omissions exposure and keeps the reviewer's attention on the hard cases.

Renewal comparisons are the second job we usually suggest. Say a 40-person agency handles a steady run of commercial renewals each month. An agent can set the expiring policy next to each quote and line up the differences in limits, deductibles, exclusions and endorsements. The account manager gets a plain summary of what changed to check before the client call. Our agents for independent agencies start with workflows like these two.

Pharma QA: deviation reports and SOP lookups

Quality teams in pharma and life sciences spend much of their week on documents. Deviation reports are a strong first candidate. When a deviation is logged, an agent can pull the batch record and the SOPs that apply, then draft the initial assessment and a proposed root cause category for the QA reviewer.

The QA reviewer stays the decision maker. The agent's draft enters the quality system only after a named person approves it, and the record keeps the reviewer's name along with every edit they made. For teams working to GxP expectations, that record carries as much weight as the draft itself.

SOP lookups are a smaller job with a quick payoff. Operators and new hires ask questions such as which version of a cleaning procedure applies to a given line. An agent that answers only from current versions in the controlled document system, and cites the exact section, saves the person asking a search and saves the QA team an interruption.

Finance and procurement: invoices and contracts

In finance, invoice matching is the classic first build. The agent reads each supplier invoice and compares it line by line with the purchase order and the goods receipt. Clean matches go to a person for a quick approval. Mismatches go to accounts payable with the specific difference spelled out, so the clerk starts from the problem instead of hunting for it.

Contract review suits legal and procurement teams that handle many similar agreements, such as NDAs and supplier terms. The agent compares each incoming contract against your standard positions and marks every clause that departs from them, with a suggested fallback from your playbook. A lawyer or contract manager still makes the call. The agent makes sure nothing in the document goes unread, and it gives the reviewer a short list of the clauses worth arguing about.

Why the first build sets up the next ones

Each of these jobs forces decisions that every later agent depends on. To draft a certificate, the agent needs read access to the agency management system and the policy documents, under its own identity and with the least access it needs. To draft a deviation assessment, it needs to see batch records while respecting who may see which product. These data access questions stall projects when they are left open, and a first build makes you answer them.

Approval decisions get made the same way. Someone has to say who signs off a certificate or an invoice exception, and the system has to record it. Once that pattern exists for one workflow, the second workflow reuses it. In our builds the second agent takes much less effort than the first, because the identity setup and the approval flow already exist.

This connects directly to the eight dimensions in our readiness check. A first agent in production moves you forward on AI in production and data access, and on human oversight and agent identity, all in one build. Measuring results improves too, since you took a baseline before you started. Keeping the prompts and rules in your own code adds model portability.

When more capable models arrive, they slot into a workflow whose access and approvals already work. You can test a new model against the same cases you used to accept the first build, and switch only if it does better. Our superintelligence readiness page shows how each area of work shifts as models improve, and our agentic AI work covers how we wire agents into existing systems with approvals.

If one of these jobs sounds like your team's week, tell us the workflow. We build it in your environment in two weeks against acceptance criteria you sign on day one, and you pay $10,000 only after every criterion is met. Independent agencies can start smaller with our five-day agency sprint, and if it cannot find 10 staff hours a week, the agency pays nothing.