Buyer’s guide · Published August 7, 2026
How to choose an AI automation agency
Short answer: choose the agency that can turn your current workflow into a testable scope, show relevant delivery evidence, explain where AI is unnecessary, document data and security controls, design failure handling, state exactly what you own, and price the complete first year. A polished model demo is not enough.
Published by Coding The Brains · Use the same questions and evidence standard for every supplier, including us.
Before contacting agencies
Write the problem before inviting a solution
Give every supplier the same brief
- • Current steps, owner, users, and systems
- • Weekly volume and time per item
- • Representative inputs and expected outputs
- • Exceptions and current failure costs
- • Data sensitivity and approval requirements
- • The measurable result and target date
Keep the architecture open
Describe the outcome and constraints without demanding an agent, model, or platform. A strong supplier should be able to recommend deterministic automation, an AI-assisted workflow, a custom agent, an existing product, or no build—and explain the tradeoff.
Use the agent vs workflow automation guide to frame that decision.
Interview scorecard
Twelve questions to ask every agency
Score each answer: 0 for absent or evasive, 1 for plausible but undocumented, and 2 for specific evidence you can place in the proposal or contract.
- 01
Will you recommend ordinary automation—or no build—when AI is unnecessary?
- Good evidence
- A credible answer starts with the business process and uses AI only where ambiguity or unstructured data makes it useful.
- Red flag
- Every proposed workflow is described as an autonomous agent.
- 02
What exactly will be delivered, and how will we accept it?
- Good evidence
- A written scope names inputs, outputs, integrations, roles, exceptions, environments, documentation, and objective acceptance tests.
- Red flag
- The deliverable is described as “an AI solution” without observable completion criteria.
- 03
What comparable systems have you shipped?
- Good evidence
- Published work, a live demonstration, code samples, architecture detail, or a confidential reference that shows production delivery—not only prototypes.
- Red flag
- Only mockups, generic demos, or outcome claims with no inspectable evidence.
- 04
Which parts are deterministic and which parts use a model?
- Good evidence
- Permissions, validation, state changes, and critical actions remain controlled by ordinary software; the model handles bounded interpretation or drafting.
- Red flag
- The model is expected to decide permissions, policy, or every workflow step.
- 05
How will our data be accessed, processed, retained, and deleted?
- Good evidence
- The supplier can name every data flow, processor, storage location, retention rule, model-provider setting, and person responsible for access.
- Red flag
- “Your data is secure” without a diagram, provider terms, or written handling rules.
- 06
What identity, permission, and approval controls will exist?
- Good evidence
- Dedicated identities, least privilege, separate read/write scopes, deterministic approval for consequential actions, and tested revocation.
- Red flag
- Shared administrator credentials or approval left to the model’s confidence.
- 07
How will the system be tested before launch?
- Good evidence
- Representative, edge, adversarial, integration, and failure cases with defined measures and recorded results against the acceptance criteria.
- Red flag
- Testing means trying a few prompts during a demo.
- 08
What happens when a model, API, or source system fails?
- Good evidence
- Timeouts, retries, idempotency where necessary, queues, fallbacks, alerts, human escalation, rollback, and an incident owner are designed upfront.
- Red flag
- The supplier assumes every external service and model response will succeed.
- 09
What will we own at handoff?
- Good evidence
- The proposal distinguishes application source code, repositories, cloud accounts, data, prompts, configuration, third-party licenses, and reusable supplier IP.
- Red flag
- Ownership is verbal, accounts stay under the supplier, or access ends when a subscription is cancelled.
- 10
How will changes be deployed and monitored?
- Good evidence
- Version control, separate environments, review, deployment records, logs, alerts, evaluation after model or prompt changes, and a rollback path.
- Red flag
- Production is edited manually with no release or regression process.
- 11
Who maintains the system after launch?
- Good evidence
- Named responsibilities, documentation, runbooks, warranty or support boundaries, response expectations, dependency updates, and an exit path.
- Red flag
- Ongoing support is either undefined or permanently mandatory.
- 12
What is the full first-year cost and what can change it?
- Good evidence
- Build fee, model/API usage, hosting, third-party tools, maintenance, support, usage assumptions, change-order rules, and cancellation terms are separated.
- Red flag
- A low setup price hides required subscriptions or undefined consumption costs.
Interpret the result
A score starts due diligence; it does not finish it
20–24
Strong documented fit
Validate references, contract language, security requirements, and the people actually assigned.
13–19
Clarification required
Convert vague answers into written deliverables, controls, evidence, owners, and commercial terms.
0–12
Material delivery risk
Do not let a strong demo compensate for missing scope, controls, ownership, testing, or support.
This scorecard is a practical comparison aid, not legal, procurement, privacy, compliance, or security advice. Increase due diligence for higher-impact uses.
Primary guidance
Independent procurement references
- Australia’s National AI Centre: Questions to ask AI suppliers — what to ask, what a good answer contains, and what to capture in writing.
- UK government guidelines for AI procurement — problem-first requirements, data assessment, transparency, testing, lifecycle management, and avoiding lock-in.
- NIST AI RMF Playbook: Manage — monitoring and documenting risks and controls for third-party AI resources.
FAQ
Choosing an AI agency
How do I choose an AI automation agency?
Choose the supplier that can define the workflow and acceptance criteria, show relevant delivery evidence, explain where AI is and is not used, document data and security controls, test failure cases, clarify ownership, and provide a complete first-year cost and handoff plan.
What is the biggest red flag when hiring an AI agency?
The biggest red flag is a solution-first pitch: selecting an agent platform or model before mapping the workflow, users, data, exceptions, risks, and measurable outcome.
Should an AI automation agency provide a proof of concept?
A proof of concept can reduce uncertainty about one difficult assumption, but it is not a production system. The proposal should state what the proof tests, what it excludes, and which controls are still required for launch.
Should we own the AI automation source code?
Ownership is valuable when the workflow is important, specialized, or expected to evolve. At minimum, the contract should clearly identify who owns application code, prompts, data, infrastructure accounts, configuration, and any reusable supplier components.
How should we compare AI agency proposals?
Give every supplier the same workflow, constraints, sample data, risk assumptions, and scoring rubric. Compare delivery evidence, scope clarity, controls, testing, ownership, support, and total cost—not the number of AI features promised.