Best practices & pitfalls

Pitfalls to avoid (they feel efficient—until they aren’t)

• Shipping without error budgets (“How wrong is acceptable?”)
• No kill switch (can’t stop a runaway loop quickly)
• No idempotency (retries cause duplicate actions)
• No versioning for prompts and routing rules (“Who changed what?”)
• No runbook (“When it breaks, what do we do at 2 a.m.?”)

Month-two survival checklist (10 steps)

1. Define the “unsafe actions”
   • Anything irreversible requires explicit approval.
2. Add structured inputs/outputs
   • Use schemas; validate fields; reject malformed payloads early.
3. Make every action idempotent
   • Store a unique execution key; safe retries become possible.
4. Implement retry policy + backoff
   • LLM/API rate limits are normal; handle them with exponential backoff.
5. Establish routing rules
   • Confidence high → auto; medium → human review; low → reject/escalate.
6. Log everything you’d need in an audit
   • Inputs, outputs, model/prompt version, tool calls, and final actions.
7. Add quality evaluation
   • Track groundedness/relevance/coherence (or task-specific metrics) over time.
8. Monitor cost and token usage
   • Token drift is a real production signal; monitor and cap usage.
9. Create an exception workflow
   • A clear queue, SLA, and owner for failed/flagged cases.
10. Ship a weekly improvement cadence

• Fix top failure modes, expand test sets, and only then broaden scope.

Performance, cost, and security considerations

Performance: latency is a product feature

• Decide your latency budget (e.g., 2s, 10s, async).
• Use caching and batching where possible.
• Put deterministic steps before LLM calls to reduce unnecessary requests.

Cost: control tokens like you control cloud spend

If you’re not watching:

• prompt length,
• retrieved context size (RAG),
• retries, and
• concurrency,

…your costs can jump quietly.

Production guidance commonly recommends monitoring token usage alongside latency and error rate—Microsoft explicitly calls this out for deployed prompt flows.

Reliability: rate limits and transient errors are normal

OpenAI’s docs explicitly recommend retry with random exponential backoff to handle rate limits effectively.

Security: treat the LLM like an untrusted component

• Least privilege tools: the model shouldn’t have broad write access.
• Audit trails: log tool calls and outcomes.
• Data boundaries: control what gets sent to model endpoints and how it’s stored/processed (especially when dealing with PII).

If you’re building AI automations that touch customer data, devices, payments, or approvals, Infolitz can help you design the guardrails and observability so the system remains safe and predictable.

Real-world use cases

Where AI automations shine (when engineered properly)

1. IoT operations
   • Triage device alerts, summarize telemetry anomalies, draft maintenance notes, propose next actions (human-approved).
2. Service desk + customer support
   • Ticket enrichment, suggested responses, root-cause hints, auto-routing.
3. DevOps workflows
   • Incident summaries, change-risk analysis, postmortem drafts with evidence links.

Mini case story (illustrative, anonymized)

A mid-sized connected-device company piloted an “AI ticket triage assistant” inside Slack. Week 1 looked great: faster summaries and nicer routing.

By week 7:

• upstream ticket fields changed,
• the model started guessing missing metadata,
• retries created duplicate updates,
• and no one owned the exceptions queue.

Fix: they reworked it into a workflow:

• schema validation + required fields,
• idempotent updates,
• confidence routing to human review,
• evaluation on a weekly test set,
• monitoring (latency, error rate, token usage).

Outcome: the automation became boring—in the best way: predictable, measurable, and safe to expand.

‍

FAQs

1) Why do AI automations succeed in a pilot but fail later?

Because pilots run on curated inputs and manual babysitting. Production introduces edge cases, integration changes, and the need for monitoring, ownership, and governance.

2) How do I take an AI automation from PoC to production?

Add durable orchestration (retries/timeouts/idempotency), guardrails, human review for risky actions, and ongoing evaluation/monitoring of quality, latency, and cost.

3) Do we have to rebuild everything from scratch?

Usually no. The fastest path is wrapping existing systems with an orchestrated workflow that validates inputs, calls the model in constrained ways, and writes back via stable APIs.

4) What’s the #1 root cause of “month-two failure”?

Lack of operational ownership + observability. Without them, failures surface late and fixes become reactive.

5) How do we monitor LLM quality in production?

Track task-specific metrics (accuracy, groundedness, relevance), plus operational metrics (latency, error rate, token usage). Microsoft documents this monitoring approach for genAI apps.

6) How do we keep costs predictable?

Cap token usage, control context size, cache where possible, and monitor token trends. Token drift is often your first signal that prompts or inputs changed.

7) Is agentic AI safe for business-critical processes?

It can be—if you constrain tools, log actions, enforce approvals, and measure outcomes. Without risk controls and clear value, many efforts get canceled.

8) What’s the difference between RPA and LLM automation?

RPA automates clicks and screens (brittle to UI change). LLM automation reasons over language and ambiguity (powerful, but needs guardrails and evaluation).

9) How long does it take to see real value after a pilot?

Typically, the hardening phase is where value becomes durable. Expect a structured ramp: pilot → harden → controlled rollout → scale.

10) What governance is “enough”?

At minimum: a process owner, an exceptions queue, audit logs, versioning, and a monitoring dashboard that covers quality + ops + cost.

‍