AI Contractor Evaluation and Acceptance for B2B

Acceptance mindset: quality bars, not vibes

Treat LLM features like any other high-risk subsystem: measurable thresholds, automated tests, security cases, and operational handover. 'Looks good in the demo' is not a gate. Separate research spikes from production milestones. Spikes may explore; milestones must meet evaluation, ACL, audit, and runbook criteria. Align delivery with testing strategy and production readiness.

• Write acceptance criteria before the build starts
• Include failure and abuse cases, not only happy paths
• Require reproducible evaluation runs on a fixed golden set
• Gate go-live on thresholds, not slide decks

Golden sets that reflect real users

Build a versioned set of inputs with expected outcomes: answers plus required citations for RAG, field extractions, classifications, or end-to-end agent trajectories. Cover the personas and tenants you actually serve. Include empty inputs, conflicting information, adversarial injections, and out-of-scope requests. Domain experts must review labels; engineers alone will miss policy nuances. For RAG-heavy work, follow the evaluation practices in production RAG. For agents, script tool outages and approval rejections as described in agentic workflows.

Evaluation thresholds and release gates

Score retrieval hit rate, citation correctness, faithfulness, task success, and unsafe action rate separately. Set minimum thresholds per environment: staging must pass before production traffic. Run regression tests on every prompt, model, chunker, reranker, or graph version bump. Block merges when thresholds drop, just as you would for flaky integration tests. Publish who can waive a gate and for how long. Permanent waivers are how demos become production debt.

• Define metrics and owners in the SOW
• Store evaluation reports as release artifacts
• Require a shadow or limited cohort before full cutover
• Track cost per successful task alongside quality gates

Demo vs production: close the gaps

Demos often use curated documents, single-tenant data, overprivileged tools, and no latency budget. Production needs ACLs, auditability, idempotent writes, observability, and support runbooks. Explicitly list demo shortcuts in the project plan and assign a paydown date. If a shortcut remains at go-live, it is accepted risk with an owner, or it is not done. Scope the first vertical with MVP prioritization and discovery via technical discovery.

ACL and security tests contractors must pass

Automate cross-tenant retrieval and tool-use tests. Verify denial logging and test prompt-injection cases through documents and tickets. Validate that schemas reject out-of-range tool arguments. Do not use a shared service role that bypasses product permissions. Use SSO-bound principals for user-facing agents. See enterprise AI security and SSO and identity. Treat a failed ACL test as a ship blocker on par with a data-breach bug in a conventional API.

Observability, audit, and runbooks

Handover should include traces (run ID, retrieval IDs, tools, approvals), dashboards, alerts, and runbooks covering reindexing, prompt/graph rollback, kill-switch activation, vendor outages, and cost-spike response. Align logs with audit logging and operations with observability and support SLAs. If only the contractor can interpret failures, you do not own the feature.

• Document on-call steps for AI incidents
• Include data and corpus ownership and refresh cadence
• Deliver the evaluation harness and instructions for extending the golden set
• Record model vendor settings and secret-handling procedures

Contract language that protects both sides

Define deliverables as systems that meet agreed thresholds, not hours of prompt tinkering. State environments, data access, IP ownership for prompts, graphs, and evaluation sets, and confidentiality requirements for customer corpora. Fixed price fits well-bounded MVPs with clear gates; T&M fits exploration with weekly demos against the golden set. See fixed price vs time and materials. Include change control when corpus quality or APIs are worse than assumed during discovery; this is often the real schedule risk. Budget with realistic cost ranges and unit economics in mind.

Human-in-the-loop and write-path acceptance

If the feature can write to systems of record, acceptance includes approval UX, evidence on the approval card, timeout behavior, idempotency, and compensation for partial failures. Draft-only can be a milestone; unsupervised writes need higher bars. Product expectations are covered in human-in-the-loop AI products. ERP and money-moving workflows also inherit ERP integration discipline: no agent gets a free pass on idempotency.

Next steps

Draft a one-page acceptance checklist: golden set size, metrics, ACL tests, audit fields, runbooks, and kill switch. Attach it to the SOW before kickoff. Continue with data readiness, other resources, case studies, book a call, or contact to review contractor proposals before you fund a demo-only engagement.

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