How to Hire an AI Agent Developer: Scope, Tests and Handover

Hire an AI agent developer when you need a working system connected to your company’s information and tools, with clear rules for what it may do. Start by describing the job and how you will accept the result. A framework name or an impressive chat demo is not enough to compare proposals.

For example, an enquiry agent might read a website conversation, establish whether someone wants your service, create a CRM record and ask a person to approve the next message. Hiring for that workflow means specifying the inputs, permissions, integrations and failure cases together.

This guide gives you a brief and review process for a custom build. It does not assume that every task needs an autonomous agent.

Decide whether custom development is necessary

A standard product may already cover the job. Test whether it can use your information, fit your approval process and connect to the required systems before commissioning replacement software.

Custom development becomes useful when those requirements do not fit the available product, or when the missing part is a connector or interface rather than another subscription. Keep useful existing tools and develop the part they cannot handle.

Anthropic’s engineering guidance distinguishes predefined workflows from agents that select their next steps dynamically. Use that distinction in the brief: a fixed sequence may suit predictable work, while an agent may help when the route changes with the request. More flexibility also creates more behaviour to test.

Your situation	A sensible starting point	What to check
One recurring question with an approved answer	Existing tool or a focused assistant	Source quality and escalation
The same steps run in the same order	A workflow integration	Inputs, validation, retries and ownership
Different requests need different permitted actions	A bounded agent	Tool selection, permissions and stopping rules
Staff need a shared queue or review screen	An internal application with AI where useful	Records, roles and the daily interface

Write a brief around one useful result

Describe what should change for the person doing the work. “Build an AI agent” is a technology request; “put qualified website enquiries in our CRM with an owner and source” describes a result the team can inspect.

Include where requests arrive, who uses the output, which systems own the data and who can approve external actions. Share representative examples with unnecessary personal information removed. Show an acceptable result and an exception, not only the easiest successful input.

InputExamplesNormal requests, incomplete information and exceptions

OutputResultThe record, answer or action a person can verify

BoundaryPermissionWhat can run, what stops and who approves

AcceptanceTestsThe checks that decide whether the system is ready

A useful enquiry brief could say: capture an explicit service request, retain the source page, ask permission to contact the visitor, create one record and notify its owner. General questions should remain conversations. Failed CRM writes should not appear as successful submissions.

Our website enquiry walkthrough illustrates that distinction with labelled fictional conversations. It is an example of FollowAI’s own intake logic, not a customer performance case.

Compare proposals using the same scope

Ask each developer to state what is included in the working system. Separate the agent’s decisions from integration work, the user interface, deployment, testing and operation after launch.

A proposal that includes only a prompt or prototype is difficult to compare with one that includes authentication, a review queue and monitoring. Both may be useful purchases, but they deliver different results.

Proposal item	What to ask for
Integrations	Named systems, supported operations and required account access
Information	Source owners, update process and behaviour when evidence is missing
Controls	Permitted actions, approval points, limits and audit records
Acceptance	Example inputs, expected outcomes and a repeatable test procedure
Deployment	Hosting owner, environments, configuration and rollback approach
Handover	Documentation, team training, support responsibility and known limitations

Test the difficult cases before release

Review useful successful requests, but also ask what happens when the request is incomplete, a source contradicts another source, the CRM is unavailable or the same request arrives twice.

For an agent that can act, test attempts to exceed its permissions. Check whether a draft stays a draft until approval, whether the review screen identifies the proposed action and whether a failed operation can be retried safely.

Define acceptable behaviour for each case before running the test. A fluent answer can still be wrong. For a workflow, the correct result may be a clarification question or a clear handoff rather than completing the action.

The NIST AI Risk Management Framework provides a general way to organise AI risk work. Referencing it does not establish certification or prove that a particular build is safe. Ask for the actual controls and evidence relevant to your system.

Separate development cost from operation

Request separate lines for the initial build, source preparation, integrations, vendor usage, hosting and optional ongoing management. Ask which assumptions change the estimate: request volume, number of channels, number of account types or complexity of permitted actions.

A small question-answering bot and a system that updates several business accounts have different work behind them. Compare the proposed deliverables instead of treating a starting price as a quote for every agent.

Also ask how the system will be changed. Source updates, new business rules, vendor changes and unexpected failures require an owner after launch. Agree whether your team handles that work or commissions continuing operation.

Make the handover usable by your team

The handover should explain how to use the system, review its output, stop an operation and report a problem. Identify the accounts, source code, configuration and documentation included in the agreed delivery.

Ask a team member who did not build it to complete a normal task and an exception. If they cannot understand the next step without the developer on the call, the handover needs more work.

Start with a controlled release and review actual errors, exceptions and adoption. Use those observations to decide what to improve; do not expand autonomy just because the demo looked convincing.

What FollowAI can build

FollowAI can design, code, connect, launch and operate the complete agent workflow: approved information, tools, permissions, a user interface, CRM or helpdesk integration, evaluation, monitoring and team onboarding. The exact deliverables and support ownership belong in the written scope.

Bring one result, your current tools and a few example requests to the custom AI agent development service. For related decisions, read AI agents versus workflows and managed agents versus a one-time automation.