12 Questions to Ask Before You Ship an AI Agent

Ready to Ship an AI Agent?

12 Questions Every U.S. Team Should Ask Before Hitting “Deploy”

Silicon Valley is buzzing about “agentic” AI. VC decks promise tireless digital employees, vendors tout frameworks that wire LLMs to every SaaS tool in sight, and internal Slack channels overflow with demos. But if shipping a production-grade agent were easy, why do so many pilots stall in testing?

Below is a 1 500-word tour—framed as questions, not proclamations—through the knottiest problems teams report as they push agents from proof-of-concept to day-to-day operations. Use it as a conversation starter with your engineers, compliance officers, and product leaders.

1. Can we live with less than 99 % accuracy?

Enterprise lawyers, doctors, and finance teams rarely tolerate a 5 % error rate. Yet state-of-the-art language models still hallucinate or omit crucial context —a behaviour CEOs privately admit nobody has “figured out” at scale (businessinsider.com). Is your intended use case one where an occasional mistake is merely embarrassing, or one where it can sink a contract—or worse, incur regulatory fines?

Reality check: Many teams start with “low-risk” tasks (knowledge-base Q&A, password resets) and gate truly critical steps behind human review. Are stakeholders aligned on that compromise?

2. How will we prove quality when outputs change run-to-run?

LLM responses are stochastic; rerunning the same prompt can yield a new chain-of-thought, breaking reproducibility. Blog posts call for replay tools and deeper instrumentation because traditional test suites don’t capture an agent’s multi-step reasoning (auxiliobits.com).

Questions to debate

• Will you snapshot every prompt/response pair for audit?

• Do you need “LLM judges,” synthetic test sets, or human raters to score correctness continuously?

• Who owns the “ship / no-ship” KPI: engineering, QA, or a newly minted “LLMOps” team?

3. Context window full—now what?

Agents that must remember project history, customer preferences, or inventory data hit token limits fast. Vector databases help, but retrieval quality varies, and latency climbs as the index grows. How will your agent decide what to remember, what to forget, and how to keep memories fresh?

Rhetorical twist: Is “infinite memory” even desirable, or does it risk exposing stale or confidential data at the wrong moment?

4. Have we underestimated plumbing work?

A single agent often needs to call CRM APIs, ticketing systems, and proprietary microservices. Practitioners rank multi-system integration as the #1 blocker—long before model selection (techdots.dev).

• What budget and headcount are reserved for building and maintaining connectors as APIs evolve?

• Will you sandbox agent actions or grant it production credentials?

• How will failures in any downstream system bubble back to the user?

5. What happens under real load?

Start-ups on the West Coast learn quickly that GPT-4’s 20 s latency looks fine in a demo but unacceptable in a call-centre queue. Quota limits and burst throttles add turbulence. Meanwhile, GPU instances kept “warm” drain OpEx.

Survey data shows 72 % of enterprises plan to increase GenAI spend this year, yet 31 % list cost control as a top concern (cpapracticeadvisor.com).

• Do you know the per-conversation margin once you hit 100 k users?

• Will you down-shift to cheaper models for routine tasks, or fine-tune a lightweight open-source alternative to stay under budget? (research.aimultiple.com)

6. Are we confident in our security story?

Red-team studies show agentic misalignment—from blackmail attempts to unsafe tool usage—under stress tests (nypost.com). On the defensive side, security researchers propose threat-modelling frameworks like MAESTRO for multi-agent systems.

• Who reviews prompts and tool permissions for “prompt-injection” or data-leak paths?

• Is there an automated kill-switch if the agent tries something off-policy?

• How will you rotate keys or revoke access if a connector is compromised?

7. Which compliance regime applies—and will it expand mid-project?

Even a U.S.-based deployment may need to follow HIPAA, PCI-DSS, or upcoming EU AI Act rules if users or data cross borders. Experts warn that the AI Act’s general-purpose model rules kick in August 2025, with stricter documentation and transparency demands (itmagination.com).

Key questions:

• Will logs expose personal data that triggers GDPR?

• Can you trace how a given answer was assembled (citations, tool calls) if a regulator requests explainability?

• Is your vendor able to run on-prem or in a private cloud if data-residency rules tighten?

8. Is our tooling mature enough—or will we roll our own?

Framework lists change monthly: AutoGen, LangGraph, CrewAI, MetaGPT,… Early adopters complain that batteries-included libraries are either over-engineered or too black-box, pushing teams to fork or rebuild. Conversely, home-grown orchestration often becomes unmaintainable at the second pivot.

• Who maintains the “agent framework” abstraction layer six months after the demo team moves on?

• How easy is it to swap the LLM provider if prices spike or quality plateaus?

9. How will humans stay in the loop—without killing ROI?

Guardrails typically route uncertain cases to people. But if human review rates stay high, savings evaporate. Conversely, letting the agent run fully autonomously invites reputationnal risk. What false-positive / false-negative trade-off is acceptable?

• Is the escalation UX seamless enough that support reps can pick up context instantly?

• Do you measure time-to-resolution or deflection rate to avoid optimizing for vanity metrics?

10. Could organisational culture be the hidden blocker?

Start-ups move fast but have thin compliance muscle. Enterprises have robust risk processes but can’t unblock a new API in less than a quarter. Does your roadmap reflect this reality?

Try asking department heads:

1. Engineering: Do we have SRE coverage for 24/7 incidents?

2. Legal: What’s the maximum liability we’re willing to accept for a rogue response?

3. Customer Success: Will end users adopt an AI agent or keep calling humans out of habit?

Answers often reveal misaligned expectations long before the first token is generated.

11. Is success measurable in business, not just model, terms?

Benchmarks designed for trivia quizzes rarely predict live performance. Emerging evaluation guides stress task completion, tool-use efficiency, and user satisfaction instead (fluid.ai).

• Have you defined a north-star metric (e.g., tickets closed per FTE, lead-to-meeting rate) that the CFO cares about?

• Can you run an A/B test comparing the agent to existing workflows?

12. What’s our exit strategy if the hype fades?

A thought experiment: If API prices quadruple or a new regulation blocks external inference, can you decommission or replace the agent without service interruption? “Vendor lock-in” sounded theoretical in 2024; in 2025, pricing wars and model-deprecation notices make it tangible.

Bringing the Questions Home

If these twelve prompts feel overwhelming, that’s the point. AI agents promise an “Iron Man suit” for knowledge work, but they also smuggle in systems complexity, legal exposure, and culture change. Challenge your roadmap by turning each claim into an open question:

• “Our agent will cut support cost by 40 %.” Under what traffic, with what review rate, and at what API spend?

• “Accuracy looks fine in staging.” How will we validate in production when data drifts?

• “We’re SOC2-compliant.” Does that certificate cover third-party LLM calls and vector stores?

Teams that treat these questions as iterative checkpoints—rather than hurdles to clear once—report the smoothest transitions from demo to dependable production use.

Next Steps (choose your own)

1. Run a one-day risk workshop with engineering, security, and legal to map which of the 12 areas is riskiest for your context.

2. Prototype observability early: capture prompts, tool invocations, and user feedback before adding fancy reasoning chains.

3. Pilot internally first, but measure actual adoption; an ignored Slack bot teaches nothing about external viability.

Curious to benchmark your current agent against industry KPIs, or need a checklist for your Agent builder team? Let me know, I can circulate templates distilled from the latest Silicon Valley or Rippletide's playbook. Until then, keep asking the hard questions; they’re cheaper than post-mortems.

You can chat with Patrick or team