Skip to content
KKoderClub
AI Engineering
7 min read Priya Nair, Principal AI Engineer

Production-grade AI agents: the checklist before you let one touch a customer

An AI agent becomes production-grade when its failure modes are bounded, observable and reversible. That requires an evaluation harness, explicit escalation to humans, a full audit trail, and a cost ceiling — none of which appear in a prototype.

Published 30 May 2026 · Updated 10 July 2026

AI AgentsLLMGovernanceEvaluation

Key takeaways

  • Evaluate against a fixed regression set before every release, exactly like software.
  • Design the escalation path first; the agent's job is to know when to stop.
  • Log every input, tool call and output for audit and dispute resolution.
  • Cap spend per conversation and per day; unbounded token cost is an operational risk.

Scope the agent to a bounded decision

Agents that succeed in enterprise settings do one bounded thing: triage an intake queue, reconcile a document against a purchase order, draft a first response for review. Agents that fail are asked to be generally helpful inside a regulated process.

Build the evaluation harness before the agent

A frozen set of two hundred representative cases with expected outcomes turns subjective judgement into a regression score. Without it, every prompt change is a guess and no release can be justified to a risk committee.

  • Golden set curated with the business owner, not by engineering alone
  • Automated scoring for structured outputs, sampled human review for judgement
  • Release gate: no deployment below the agreed threshold

Human-in-the-loop is a design decision, not a fallback

Decide upfront which outcomes the agent may action autonomously, which it may draft for approval and which it must escalate untouched. Publish that matrix to the operations team. Confidence thresholds without a written matrix create ambiguity precisely when something goes wrong.

Observability, cost and rollback

Every tool call should be logged with inputs, outputs, latency and token spend, retained for the audit window the business requires. Set per-conversation and per-day ceilings, and keep the previous prompt and model version deployable behind a flag.

The twelve controls in short

Use this as the pre-go-live gate.

  • Bounded scope with a written decision matrix
  • Frozen evaluation set and release threshold
  • Escalation path with named owners
  • Full audit trail with retention policy
  • PII handling and redaction rules
  • Cost ceilings per conversation and per day
  • Latency budget and graceful degradation
  • Model and prompt version pinning
  • One-click rollback
  • Prompt-injection and tool-abuse testing
  • Operator training and a feedback channel
  • Post-deployment drift review on a schedule

Apply this

Get this reviewed against your own systems

Send us the constraint you are working on. A senior practitioner responds with an approach note, phased plan and the metrics worth committing to.

Ask about this topic

Share your current systems and constraints. A senior consultant responds within one business day, under NDA.

NDA friendly. We never share your details.

FAQ

Questions readers ask about this topic

CallBook a consultation