From pilot to production: an honest checklist
The pilot impressed your leadership. Production will impress your auditors — if you let it.
Why most agentic pilots stall before production
Most agentic AI pilots produce a 'wow' moment, a strong sponsor briefing, and then plateau. The plateau is usually not a technology problem — it's a readiness problem. The same agent that performed well on a curated demo dataset has to survive auth, audit, RBAC, change management, model risk, on-call rotation, cost control and compliance review before it can run unattended on Tuesday morning.
This post collects the readiness questions we ask every enterprise team when we move them from pilot to production. None of it is glamorous. All of it is what separates 'cool demo' from 'mission-critical infrastructure'.
What teams underestimate
Each of these is a workstream, not a checkbox.
Identity & access
Every agent action must be bound to a real principal — not a service account. RBAC scopes, delegation, attribute-based rules, and audit-on-every-call are not optional in regulated environments.
Model risk
If your bank, insurer or healthcare org has a model-risk function, agents are models — versioned, tested, governed, monitored, attested. Plan for the review cycle from day one.
Operations & SRE
Self-healing helps, but production agents still need on-call rotations, runbooks, incident-response procedures, blameless post-mortems and SLOs.
Cost transparency
Token costs, tool-call costs, downstream API costs — all visible, attributable to workflows, ideally with budgets. Without this, the CFO will eventually pull the plug.
Ten things to confirm before go-live
If you can't tick all ten, you're not ready for production — and the regulator will eventually agree.
- Every action is identity-bound and RBAC-scoped (no service-account agents).
- Every reasoning step, tool call and approval is captured in a tamper-evident audit trail.
- Destructive operations require explicit human approval, with full context.
- Agent versions are immutable, with reproducible test results from a regression suite.
- Rollback to any prior version is one-click and rehearsed.
- Live telemetry covers latency, cost, quality and policy-decision metrics.
- Anomalies (drift, cost spikes, policy denials) trigger the right team in the right channel.
- Compliance evidence packs export on demand for SOC 2 / ISO 27001 / HIPAA / EU AI Act.
- Data residency and sovereignty rules are enforced at the platform layer, not in app code.
- An incident-response runbook exists for each agent class, and on-call has rehearsed it.
This list looks long because it is
The list looks long because production is long. The good news is that most of it is platform work — solve it once, every subsequent agent inherits it. The bad news is that most early agent frameworks didn't design for it, so retrofitting can be expensive.
The teams that get to production fastest are the ones that pick a platform that already addresses the platform-level concerns, and reserve their effort for the genuinely domain-specific work. That's where the leverage is.
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