Yes, for the parts that matter most. A business team can describe the job in plain language, choose the sources, supply real cases to test against and propose limits, which is most of what makes an agent good. Connecting systems, creating its identity and approving it still need security, technology and risk. Building is not deploying.
What decides whether a business team can do it?
Whether your platform separates describing the job from granting authority. If building an agent means writing code and wiring credentials, a business team cannot do it safely. If it means describing the work, choosing from approved connections and testing on cases, while authority comes from a separate approval route, they can.
The second arrangement is also better for the agent. The people who know which cases are hard, which customers need care and which exceptions recur are in the business team. When engineers write the brief from a requirements document, much of that knowledge is lost.
Who does which part?
| Step | Business team | Technical and risk teams |
|---|---|---|
| Describe the job | Writes it in plain language | Reviews for clarity |
| Choose sources | Names the policies, procedures and records | Confirms each source is registered and current |
| Connect systems | Says what the agent needs to read and change | Builds or approves the connections |
| Identity and access | States what access the job needs | Creates the agent's own identity and grants access |
| Set limits | Proposes amounts, hand-over rules, never list | Risk approves |
| Test | Supplies real past cases and judges results | Runs certification; reviews results |
| Approve for production | Asks for approval with evidence | Risk and the accountable executive approve |
| Run | Owns the agent day to day | Supports the platform |
The identity row is easy to skip. An agent needs its own non-human identity, created by security, not a team member's login. A business team that builds an agent on someone's personal credentials has built a shadow agent, however good its intentions.
What does this look like in practice?
Take a motor insurer's claims team. Adjusters spend hours chasing documents: repair estimates, photos, police references. The team lead wants an agent to send document requests and chase them.
She describes the job in plain language: read new claims, work out which documents are missing for that claim type, draft a request to the policyholder, chase after five days, and flag claims still missing documents after ten. She names the sources: the claims procedure, the document checklist per claim type, and the claim record. She picks two hundred closed claims as test cases.
Security creates the agent's identity with read access to claims and permission to create drafts in the outbound mailbox. Testing shows the agent misses that some claim types need a police reference only if a third party was involved. The team lead adds that rule to the checklist source, the agent is re-tested, and risk approves it at depth 2: it drafts, an adjuster sends. Turning the description into an agent is covered in how to build an AI agent from a plain-language job description.
Who answers for the result?
The business team's leader, as the agent's owner. Technology answers for the connections and identity; risk answers for its approval. The owner answers for the agent's decisions in their process, which is why it makes sense for them to have shaped the agent in the first place.
What controls make it safe?
- Building and authority are separate. A team can build a candidate; only approval puts it into production.
- Approved connections only. Teams choose from a catalogue technology maintains, not their own integrations.
- Own identity per agent, created by security.
- Testing on real cases before any action, judged by the people who know the work.
- Start at a low depth. Drafts first; execution only on evidence.
Whether to build at all, or buy agents pre-trained for common jobs, is a separate decision; see should you build or buy AI agents.
Where Pryme fits
In Pryme, Agent Studio lets a team describe the job in plain language and assembles the agent's brief: role, sources and connections. That produces a candidate, not a deployed agent. Attest then tests it on the team's own cases and sets its depth. More in deploying an AI agent: train, shadow, certify, widen.
Questions people ask
Do non-technical teams need training first?
A little. They need to understand depth, limits and how testing works, and to practise writing a job description precise enough to test. They do not need to learn to code.
Can they change their agent after it goes live?
They can propose changes. A change to sources, limits or the brief goes through change control and is re-tested before release.
What if the team wants a capability the platform does not support?
Then it becomes a technology project, with the business team still owning the description and the test cases.