How to evaluate LLM agents & RAG AI partners
Look for a comparable deployed workflow and the team that actually built it. A client logo or polished model demo is weaker evidence than the integration, acceptance test, failure handling and support record.
| Evaluation criterion | What to ask / useful evidence | Warning sign |
|---|---|---|
| Comparable grounded applications | Show a deployed answer or task backed by inspectable evidence.Approved sources or confirmed actions linked to user-request traces. | The demonstration relies on public facts unrelated to the buyer’s material. |
| Retrieval and permission evaluation | How do you distinguish a retrieval miss from a generation error?Component results and tests using inaccessible, missing and conflicting documents. | A single chatbot satisfaction score replaces source-level evaluation. |
| Agent necessity and action control | Which tasks benefit from multi-step planning, and what limits the actions?A simpler baseline comparison, scoped tool permissions and tested stop conditions. | More agents are presented as evidence of a better solution. |
| Cost and source lifecycle ownership | Can we inspect consumption and remove a source completely?Per-task traces, access updates, deletion tests and exportable configuration. | The partner cannot explain index freshness or failed-loop costs. |
What should you ask for in a demo or free trial?
- 01Check comparable AI work
- 02Test a representative case
- 03Compare the full proposal
- 04Agree acceptance & exit
Ask whether a no-cost demonstration, sandbox trial or sample-data evaluation is available. A free consultation does not mean free implementation; many custom AI pilots are paid. Confirm any trial limit, data handling, ownership of outputs and charges before supplying material.
Make the demonstration relevant: Internal knowledge assistant
- Bring this evidence
- Versioned documents, realistic questions and role permissions.
- Ask to see this result
- A retrieval pipeline, cited answers and an evaluation set of answerable and unanswerable questions.
- Introduce this failure
- The answer cites a passage that contains similar words but does not support the conclusion.
- Discuss acceptance
- Relevant-source retrieval, supported claims and access-isolation failures.
- One workflow, a representative sample and a named reviewer.
- What is free, what is paid, usage limits and when billing begins.
- Access permissions, retention, deletion and permission to use sample data.
- The acceptance evidence, deliverables, ownership and the exit path.
Your LLM agents & RAG AI hiring checklist
Use these evidence checks during supplier discussions. Download the topic-specific PDF for a review meeting or to keep a separate copy for each candidate.
- 01Define supported questions, allowed actions and refusal boundaries.
- 02Inspect comparable production RAG or agent evidence and failure traces.
- 03Test source permissions, update propagation and business-tool access.
- 04Estimate tokens, embeddings, retrieval, storage and review costs per successful task.
- 05Validate reusable ingestion and agent components against your sources.
- 06Confirm code, index configuration, evaluation data and supplier-account ownership.
- 07Train users on evidence checking and source owners on approved updates.
- 08Accept evaluation suites, deletion procedures, runbooks and maintenance responsibilities.
Your topic-specific checklist & buyer pack
AI scope, integration, cost questions, evaluation evidence, training and handover.
What should the proposal commit to?
| Milestone | Evidence required for acceptance |
|---|---|
| Question and collection study | The content owner agrees which collection is authoritative and sufficiently complete. |
| Retrieval baseline | Relevant evidence reaches the response stage consistently on the held-out questions. |
| Grounded response and tool tests | The system abstains, restricts or escalates where evidence or authority is absent. |
| Observed workflow release | Operators can identify the source and action versions behind a disputed result. |
Separate fixed scope, change requests, external subscriptions, support coverage and warranty fixes. Confirm source-code and data ownership, access to accounts, documentation, training, acceptance responsibility and an exit handover.
Staff training, documentation & handover
Name the people who will operate the AI workflow after launch. Put the following deliverables into the statement of work, with an acceptance owner and a separately identified training or handover allowance.
Staff training & adoption
- Train users to inspect evidence, recognize unsupported answers and escalate an incorrect action with its request trace.
- Teach source owners to publish approved changes and operators to run evaluation cases before expanding tool access.
Documents & technical assets
- Provide the source inventory, ingestion rules, permission model and deletion procedure.
- Deliver prompts, tool schemas, model settings, deployment code and evaluation datasets with clear ownership.
- Document cost limits, incident triage, model changes, rollback and human escalation.
Support & maintenance
- Monitor unsupported answers, denied actions, stalled loops and unusual cost per completed task.
- Maintain source connectors, permission propagation and regression evaluations when models or business tools change.
AI partner evaluation FAQs
What should an LLM or RAG developer demonstrate with our own material?
Provide an unseen question, a restricted document and an unsupported request. Inspect retrieved evidence, the answer’s claim support and the access behavior. If the assistant can act, also test a failed tool call and stopping condition. These tests distinguish application engineering from a polished prompt connected to a convenient document sample.
Which reusable agent components can reduce development effort?
Connectors, retrieval modules, evaluation harnesses and review interfaces may be reusable if they fit your access and workflow requirements. Ask which pieces are pre-existing, licensed or custom-built and what must be retested on your data. Confirm modification, export and support rights before treating an accelerator as a durable saving.
What handover should we require from an agent development partner?
Require source-connector configuration, prompt and tool definitions, evaluation cases, deployment instructions and cost observability. Document source ownership, permissions, action boundaries and failure escalation. Have your team update a source and investigate a failed task during handover so maintenance does not depend on undocumented knowledge held by the original developer.
Sources behind the hiring guidance
Technical references inform the workflow context. The hiring questions, checklist and acceptance guidance are RankGlobal editorial analysis. Verify the proposed team’s actual implementation role, references, evidence and contractual responsibilities before appointing a supplier.